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All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

Requirements, Resource Planning, and Management for Decrewing/Recrewing Scenarios of the International Space Station

Following the failure of 44 Progress (44P) on launch in August 2011, and the subsequent grounding of all Russian Soyuz rocket based launches, the International Space Station (ISS) ground teams engaged in an effort to determine how long the ISS could remain crewed, what would be required to safely configure the ISS for decrewing, and what would be required to recrew the ISS upon resumption of Soyuz rocket launches if decrewing became necessary. This White Paper was written to capture the processes and lessons learned from real-time time events and to provide a reference and training document for ISS Program teams in the event decrewing of the ISS is needed. Through coordination meetings and assessments, teams identified six decrewing priorities for ground and crew operations. These priorities were integrated along with preflight priorities through the Increment re-planning process. Additionally, the teams reviewed, updated, and implemented changes to the governing documentation for the configuration of the ISS for a contingency decrewing event. Steps were taken to identify critical items for disposal prior to decrewing, as well as identifying the required items to be strategically staged or flown with the astronauts and cosmonauts who would eventually recrew the ISS. After the successful launches and dockings of both 45P and 28 Soyuz (28S), the decrewing team transitioned to finalizing and publishing the documentation for standardizing the decrewing flight rules. With the continued launching of crews and cargo to the ISS, utilization and science is again a high priority; both Increment pairs 29 and 30, and Increment 31 and 32 reaching the milestone of at least 35 hours per week average utilization.

Bach, David A.↗
NASA NTRS2022

Multisensor Machine Learning to Retrieve High Spatiotemporal Resolution Land Surface Temperature

Climate change is making heat waves more frequent, long-lasting, and severe. While multiple satellite types provide data to monitor surface temperature, geostationary (GEO) sensors provide near-continuous, continental-scale observations which can better capture the diurnal variability of land surface temperature (LST) than intermittent observations from low-earth orbit (LEO) sensors. However, standard products from GEO satellites are available at coarsened spatial and temporal resolutions compared to the native sensor resolution. Using datasets from the NASA Earth Exchange, we leveraged co-located, co-temporal observations from LEO and GEO satellites to learn a data-driven mapping using a convolutional neural network. The resulting NASA Earth eXchange Artificial Intelligence LST (NEXAI-LST) achieved a mean absolute error of 1.73 K relative to the target LEO product and improves on both spatial and temporal resolution [2 km, 10 minute] compared to the GEO full disk standard product [10 km, hourly]. In validation against measurements from a ground-based sensor network, NEXAI-LST achieves similar or better fit than both LEO and GEO standard products, while depending none of the prior knowledge of land surface and atmospheric states required by physical-statistical models. Further, application of the model to unseen LEO and GEO satellites demonstrates robust generalization of the model across spatial region, time of day, and sensor. In support of NASA’s open-source science initiative, we make our NEXAI-LST product, model, and codes available to facilitate data exploration and further studies.

Kate Marie Duffy↗
DOE OSTI2025

A derecho climatology (2004–2021) in the United States based on machine learning identification of bow echoes

Due to their persistent widespread severe winds, derechos pose significant threats to human safety and property, with impacts comparable to many tornadoes and hurricanes. Yet, automated detection of derechos remains challenging due to the absence of spatiotemporally continuous observations and the complex criteria employed to define the phenomenon. This study presents an objective derecho detection approach capable of automatically identifying derechos through both observations and model results. The approach is grounded in a physically based definition of derechos and integrates three algorithms: (1) the Python Flexible Object Tracker (PyFLEXTRKR) algorithm to track mesoscale convective systems (MCSs), (2) a semantic segmentation convolutional neural network to identify bow echoes, and (3) a comprehensive classification algorithm to detect derechos within MCS life cycles and distinguish derecho-producing from non-derecho-producing MCSs. Using this approach, we developed a novel high-resolution (4 km and hourly) observational dataset of derechos and accompanying derecho-producing MCSs over the United States east of the Rocky Mountains from 2004 to 2021. The dataset consists of two subsets based on different gust speed data sources and is analyzed to document the climatology of derechos in the United States. On average, 12–15 derechos are identified per year, aligning with previous estimations (∼6–21 events annually). The spatial distribution and seasonal variation patterns are consistent with prior studies, showing peak occurrences in the Great Plains and the Midwest during the warm season. Additionally, during the study period, derechos account for approximately 3.1 % of measured damaging gusts (≥25.93 m s−1) over the eastern United States. The dataset is publicly available at https://doi.org/10.5281/zenodo.14835362 (Li et al., 2025).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Towards a self-driving trigger at the LHC: adaptive response in real time

Real-time data filtering and selection—or trigger—systems at high-throughput scientific facilities such as the experiments at the Large Hadron Collider must process extremely high-rate data streams under stringent bandwidth, latency, and storage constraints. Yet these systems are typically designed as static, hand-tuned menus of selection criteria grounded in prior knowledge and simulation. In this work, we further explore the concept of a self-driving trigger, an autonomous data-filtering framework that reallocates resources and adjusts thresholds dynamically in real-time to optimize signal efficiency, rate stability, and computational cost as instrumentation and environmental conditions evolve. We introduce a benchmark ecosystem to emulate realistic collider scenarios and demonstrate real-time optimization of a menu including canonical energy sum triggers as well as modern anomaly-detection algorithms that target non-standard event topologies using machine learning. Using simulated data streams and publicly available collision data from the Compact Muon Solenoid experiment, we demonstrate the capability to dynamically and automatically optimize trigger performance under specific cost objectives without manual retuning. Our adaptive strategy shifts trigger design from static menus with heuristic tuning to intelligent, automated, data-driven control, unlocking greater flexibility and discovery potential in future high-energy physics analyses.

Emami, Shaghayegh [Michigan U.] (ORCID:00090007589↗
DOE OSTI2021

Fermionic Partial Tomography via Classical Shadows

Here we propose a tomographic protocol for estimating any k-body reduced density matrix (k-RDM) of an n-mode fermionic state, a ubiquitous step in near-term quantum algorithms for simulating many-body physics, chemistry, and materials. Our approach extends the framework of classical shadows, a randomized approach to learning a collection of quantum-state properties, to the fermionic setting. Our sampling protocol uses randomized measurement settings generated by a discrete group of fermionic Gaussian unitaries, implementable with linear-depth circuits. We prove that estimating all k-RDM elements to additive precision ϵ requires on the order of ($^{n}_{k}$)k 3/2 log(n)/ϵ 2 repeated state preparations, which is optimal up to the logarithmic factor. Furthermore, numerical calculations show that our protocol offers a substantial improvement in constant overheads for k ≥ 2, as compared to prior deterministic strategies. We also adapt our method to particle-number symmetry, wherein the additional circuit depth may be halved at the cost of roughly 2–5 times more repetitions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Performance Evaluation of an Occupancy-Based HVAC Control System in an Office Building

As new algorithms incorporate occupancy count information into more sophisticated HVAC control, these technologies offer great potential for reductions in energy costs while enhancing flexibility. This study presents results from a two-year field evaluation of an occupancy-based HVAC control system installed in an office building. Two wings on each of the building’s 2–11 floors were equipped with occupancy counters to learn occupancy patterns. In combination with proprietary machine learning algorithms and thermal modeling, the occupancy data were leveraged to implement optimized start, early closure, and adjustments to fan operation at the air handling unit (AHU) level. This study conducted a holistic evaluation of technical performance, cost-effectiveness analysis, and user satisfaction. Results show the platform reduced weekday AHU run times by 2 h and 35 min per AHU per day during the pandemic time period. Simulation shows that 6.1% annual whole-building savings can be achieved when the building is fully occupied. The results are compared with prior studies, and potential drivers are discussed for future opportunities. The assessment results shed light on the expected in-the-field performance for researchers and industry stakeholders and enabled practical considerations as the technology strives to move beyond research-grade pilot trials into product-grade deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Dynamically Learning Incentives for Load Control

As electrical generation becomes more distributed and volatile, and loads become more uncertain, controllability of distributed energy resources (DERs), regardless of their ownership status, will be necessary for grid reliability. Grid operators lack direct control over end-users' grid interactions, such as energy usage, but incentives can influence behavior -- for example, an end-user that receives a grid-driven incentive may adjust their consumption or expose relevant control variables in response. A key challenge in studying such incentives is the lack of data about human behavior, which usually motivates strong assumptions, such as distributional assumptions on compliance or rational utility-maximization. In this paper, we propose a general incentive mechanism in the form of a constrained optimization problem -- our approach is distinguished from prior work by modeling human behavior (e.g., reactions to an incentive) as an arbitrary unknown function. We propose feedback-based optimization algorithms to solve this problem that each leverage different amounts of information and/or measurements. We show that each converges to an asymptotically stable incentive with (near)-optimality guarantees given mild assumptions on the problem. Finally, we evaluate our proposed techniques in voltage regulation simulations on standard test beds. We test a variety of settings, including those that break assumptions required for theoretical convergence (e.g., convexity, smoothness) to capture realistic settings. In this evaluation, our proposed algorithms are able to find near-optimal incentives even when the reaction to an incentive is modeled by a theoretically difficult (yet realistic) function.

demand response↗
DOE OSTI2023

Using electronic health record metadata to predict housing instability amongst veterans

Housing instability is considered a significant life stressor and preemptive screening should be applied to identify those at risk for homelessness as early as possible so that they can be targeted for specialized care. We developed models to classify patient outcomes for an established VA Homelessness Screening Clinical Reminder (HSCR), which identifies housing instability, in the two months prior to its administration. Logistic Regression and Random Forest models were fit to classify responses using the last 18 months of document activity. We measure concentration of risk across stratifications of predicted probability and observe an enriched likelihood of finding confirmed false negative responses from veterans with diagnosed housing instability. Positive responses were 34 times more likely to be detected within the top 1 % of patients predicted at risk than from those randomly selected. There is a 1 in 4 chance of detecting false negatives within the top 1 % of predicted risk. Machine learning methods can classify between episodes of housing instability using a data-driven approach that does not rely on variables curated from domain experts. This method has the potential to improve clinicians’ ability to identify veterans who are experiencing housing instability but are not captured by HSCR.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1994

Fourth NASA Goddard Conference on Mass Storage Systems and Technologies

This report contains copies of all those technical papers received in time for publication just prior to the Fourth Goddard Conference on Mass Storage and Technologies, held March 28-30, 1995, at the University of Maryland, University College Conference Center, in College Park, Maryland. This series of conferences continues to serve as a unique medium for the exchange of information on topics relating to the ingestion and management of substantial amounts of data and the attendant problems involved. This year's discussion topics include new storage technology, stability of recorded media, performance studies, storage system solutions, the National Information infrastructure (Infobahn), the future for storage technology, and lessons learned from various projects. There also will be an update on the IEEE Mass Storage System Reference Model Version 5, on which the final vote was taken in July 1994.

Kobler, Benjamin↗
DOE OSTI2020

Technical note: Deep learning for creating surrogate models of precipitation in Earth system models

Abstract. We investigate techniques for using deep neural networks to produce surrogatemodels for short-term climate forecasts. A convolutional neural network istrained on 97 years of monthly precipitation output from the 1pctCO2 run (theCO 2 concentration increases by 1 % per year) simulated by the second-generation Canadian Earth System Model (CanESM2). The neural network clearly outperforms a persistence forecast anddoes not show substantially degraded performance even when the forecast lengthis extended to 120 months. The model is prone to underpredicting precipitationin areas characterized by intense precipitation events. Scheduled sampling(forcing the model to gradually use its own past predictions rather than groundtruth) is essential for avoiding amplification of early forecasting errors.However, the use of scheduled sampling also necessitates preforecasting(generating forecasts prior to the first forecast date) to obtain adequateperformance for the first few prediction time steps. We document the trainingprocedures and hyperparameter optimization process for researchers who wish toextend the use of neural networks in developing surrogate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Attribution of heterogeneous stress distributions in low-grain polycrystals under conditions leading to damage

In high-purity polycrystalline metallic materials, voids tend to favor grain boundaries as nucleation sites due to the elevated stress states produced by granular interactions and the weakened grain boundary from the relative atomic disorder. To quantify the key factors of this elevated stress state, simple compression of a small multi-grain cylinder of body-centered cubic tantalum was simulated using a single crystal plasticity model that incorporates non-Schmid effects. Four increasingly complex synthetic microstructures were created to tractably incorporate grain boundary interactions, and a statistically significant number of combinations were performed by varying the initial crystallographic orientations of the microstructure. Most of these simulations produce the maximum von Mises stress on a grain boundary and less frequently at the multi-grain junctions. To build a statistical model for the maximum von Mises stress at the grain boundary, physically based features that could contribute to the elevated stress state were selected. Then, a learning algorithm based on information theory was used to identify which of these features contributed the most information to the data set. The identified features include a grain’s propensity to accommodate both elastic and plastic deformations and their directional components. The misalignment of the direction of each grain’s mechanical response was found to be strongly correlated to the magnitude of the stress near the grain boundary. For all of the synthetic microstructures, the statistical models produce a residual distribution that is nearly Gaussian with a variance of, at most, 10% of the prior distribution. The successful performance of the statistical model implies the correct identification of the physical features that cause severe stress localization in polycrystalline materials. The statistical models constructed here can be used to formulate a physically motivated void nucleation model which is sensitive to a microstructure’s propensity to produce elevated stress states. As a result, these statistical models also enable the design of material microstructures, in which the crystallographic orientation is chosen to resist void nucleation.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Developing new analysis tools for near surface radio-based neutrino detectors

The ARIANNA experiment is an Askaryan radio detector designed to measure high-energy neutrino induced cascades within the Antarctic ice. Ultra-high-energy neutrinos above 10 16 eV have an extremely low flux, so experimental data captured at trigger level need to be classified correctly to retain as much neutrino signal as possible. We first describe two new physics-based neutrino selection methods, or "cuts", (the updown and dipole cut) that extend the previously published analysis to a specialized ARIANNA station with 8 antenna channels, which is double the number used in the prior analysis. For a standard trigger with a threshold signal to noise ratio at 4.4, the new cuts produce a neutrino efficiency of > 95% per station-year of operation, while rejecting 99.93% of the background (corresponding to 53 remaining experimental background events). When the new cuts are combined with a previously developed cut using neutrino waveform templates, all background is removed at no change of efficiency. In addition, the neutrino efficiency is extrapolated to 1,000 station-years of operation, obtaining 91%. This work then introduces a new selection method (the deep learning cut) to augment the identification of neutrino events by using deep learning methods and compares the efficiency to the physics-based analysis. The deep learning cut gives 99% signal efficiency per station-year of operation while rejecting 99.997% of the background (corresponding to 2 remaining experimental background events), which are subsequently removed by the waveform template cut at no significant change in efficiency. Here the results of the deep learning cut were verified using measured cosmic rays which shows that the simulations do not introduce artifacts with respect to experimental data. The paper demonstrates that the background rejection and signal efficiency of near surface antennas meets the requirements of a large scale future array, as considered in baseline design of the radio component of IceCube-Gen2.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

James Webb Space Telescope: Supporting Multiple Ground System Transitions in One Year

Ideas, requirements, and concepts developed during the very early phases of the mission design often conflict with the reality of a situation once the prime contractors are awarded. This happened for the James Webb Space Telescope (JWST) as well. The high level requirement of a common real-time ground system for both the Integration and Test (I&T), as well as the Operation phase of the mission is meant to reduce the cost and time needed later in the mission development for re-certification of databases, command and control systems, scripts, display pages, etc. In the case of JWST, the early Phase A flight software development needed a real-time ground system and database prior to the spacecraft prime contractor being selected. To compound the situation, the very low level requirements for the real-time ground system were not well defined. These two situations caused the initial real-time ground system to be switched out for a system that was previously used by the Bight software development team. To meet the high-!evel requirement, a third ground system was selected based on the prime spacecraft contractor needs and JWST Project decisions. The JWST ground system team has responded to each of these changes successfully. The lessons learned from each transition have not only made each transition smoother, but have also resolved issues earlier in the mission development than what would normally occur.

Detter, Ryan↗
DOE OSTI2021

End-to-End Differentiable Modeling and Management of the Environment

Focal Area: (2) Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization. We emphasize the importance of leveraging optimization techniques from AI/machine learning (ML) to solve challenging problems in Earth system modeling. Science Challenge: Automatic differentiation has had a transformative effect on ML by allowing the calculation of gradients of arbitrary functions in an incredibly large class of models. We can potentially realize similar improvements in parameter estimation and control for Earth system models (ESMs) by reimplementing them in computational frameworks from ML. Practitioners working with large (>10 7 parameters) models in ML and AI can obtain good predictive performance in a range of spatiotemporal tasks by making use of optimization via stochastic gradient descent and incorporating prior knowledge at multiple levels. We propose writing ESMs in open-source computational frameworks such as Torch, Tensorflow, and JAX to greatly expand the scope of environmental forecasting and management challenges, which can be addressed by leveraging automatic differentiation and gradient descent-like algorithms. We do not call for a wholesale replacement of physical models with data-driven surrogates, but rather advocate for interleaving physical and empirical equations in a manner that is most faithful to the extent of our scientific knowledge and observational data. Central to this topic is the merging of differentiable physical simulations with differentiable optimization layers, which are now both beginning to come to the forefront.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2001

Use of a Scale Model in the Design of Modifications to the NASA Glenn Icing Research Tunnel

Major modifications were made in 1999 to the 6- by 9-Foot (1.8- by 2.7-m) Icing Research tunnel (IRT) at the NASA Glenn Research Center, including replacement of its heat exchanger and associated ducts and turning vanes, and the addition of fan outlet guide vanes (OGV's). A one-tenth scale model of the IRT (designated as the SMIRT) was constructed with and without these modifications and tested to increase confidence in obtaining expected improvements in flow quality around the tunnel loop. The SMIRT is itself an aerodynamic test facility whose flow patterns without modifications have been shown to be accurate, scaled representations of those measured in the IRT prior to the 1999 upgrade program. In addition, tests in the SMIRT equipped with simulated OGV's indicated that these devices in the IRT might reduce flow distortions immediately downstream of the fan by two thirds. Flow quality parameters measured in the SMIRT were projected to the full-size modified IRT, and quantitative estimates of improvements in flow quality were given prior to construction. In this paper, the results of extensive flow quality studies conducted in the SMIRT are documented. Samples of these are then compared with equivalent measurements made in the full-scale IRT, both before and after its configuration was upgraded. Airspeed, turbulence intensity, and flow angularity distributions are presented for cross sections downstream of the drive fan, both upstream and downstream of the replacement flat heat exchanger, in the stilling chamber, in the test section, and in the wakes of the new comer turning vanes with their unique expanding and contracting designs. Lessons learned from these scale-model studies are discussed.

Canacci, Victor A.↗
NASA NTRS2001

Geoscience Laser Altimeter System (GLAS) Instrument: Flight Loop Heat Pipe (LHP) Acceptance Thermal Vacuum Test

Two loop heat pipes (LHPs) are to be used for tight thermal control of the Geoscience Laser Altimeter System (GLAS) instrument, planned for flight in late 2001. The LHPs are charged with Propylene as a working fluid. One LHP will be used to transport 110 W from a laser to a radiator, the other will transport 160 W from electronic boxes to a separate radiator. The application includes a large amount of thermal mass in each LHP system and low initial startup powers. The initial design had some non-ideal flight design compromises, resulted in a less than ideal charge level for this design concept with a symmetrical secondary wick. This less than ideal charge was identified as the source of inadequate performance of the flight LHPs during the flight thermal vacuum test in October of 2000. We modified the compensation chamber design, re-built and charged the LHPs for a final LHP acceptance thermal vacuum test. This test performed March of 2001 was 100% successful. This is the last testing to be performed on the LHPs prior to instrument thermal vacuum test. This sensitivity to charge level was shown through varying the charge on a Development Model Loop Heat Pipe (DM LHP) and evaluating performance at various fill levels. At lower fills similar to the original charge in the flight units, the same poor performance was observed. When the flight units were re-designed and filled to the levels similar to the initial successful DM LHP test, the flight units also successfully fulfilled all requirements. This final flight Acceptance test assessed performance with respect to startup, low power operation, conductance, and control heater power, and steady state control. The results of the testing showed that both LHPs operated within specification. Startup on one of the LHPs was better than the other LHP because of the starter heater placement and a difference in evaporator design. These differences resulted in a variation in the achieved superheat prior to startup. The LHP with the lower superheat was sensitive to the thermal environment around the compensation chamber, while the LHP with the higher superheat (similar in design to DM LHP) was not. In response to the test results the placement of the starter heater will be optimized for the flight instrument testing for higher achieved superheat. This presentation discusses startup behavior, overall conductance of a radiator system, low power operation, high power operation, temperature control stability, and control heater power requirements as measured during this acceptance thermal vacuum test. A brief summary of 'lessons learned' will be included.

Baker, Charles↗
NASA NTRS2019

European Service Module - Structural Test Article (E-STA) Building Block Test Approach and Model Correlation Observations

The Orion European Service Module - Structural Test Article (E-STA) underwent sine vibration testing in 2016 using the Mechanical Vibration Facility (MVF) multi-axis shaker system at NASA Glenn Research Center’s (GRC) Plum Brook Station (PBS) Space Power Facility (SPF). The main objective was to verify the structural integrity of the European Service Module (ESM) under sine sweep dynamic qualification vibration testing. A secondary objective was to perform a fixed-base modal survey, while E-STA was still mounted to MVF, in order to achieve a test correlate the finite element model (FEM). To facilitate the E-STA system level correlation effort, a building block test approach was implemented. Modal tests were performed on two major subassemblies, the crew module/launch abort structure (CM/LAS) and the crew module adapter (CMA) mass simulators. These subassembly FEMs were individually correlated and then integrated into the E-STA FEM prior to the start of the E-STA sine vibration test. This paper summarizes the modal testing and model correlation efforts of both of these subassemblies and how the building block approach assisted in the overall correlation of the E-STA FEM. This paper will also cover modeling practices that should be avoided, recommended instrumentation positioning on complex structures, and the importance of the FEM geometrically matching CAD in sufficient detail in order to adequately replicate internal load paths. The goal of this paper is to inform the reader of the hard earned lessons learned and pitfalls to avoid when applying a building block test approach.

Winkel, James P.↗
NASA NTRS1992

The adult literacy evaluator: An intelligent computer-aided training system for diagnosing adult illiterates

An important part of NASA's mission involves the secondary application of its technologies in the public and private sectors. One current application being developed is The Adult Literacy Evaluator, a simulation-based diagnostic tool designed to assess the operant literacy abilities of adults having difficulties in learning to read and write. Using ICAT system technology in addition to speech recognition, closed-captioned television (CCTV), live video and other state-of-the art graphics and storage capabilities, this project attempts to overcome the negative effects of adult literacy assessment by allowing the client to interact with an intelligent computer system which simulates real-life literacy activities and materials and which measures literacy performance in the actual context of its use. The specific objectives of the project are as follows: (1) To develop a simulation-based diagnostic tool to assess adults' prior knowledge about reading and writing processes in actual contexts of application; (2) to provide a profile of readers' strengths and weaknesses; and (3) to suggest instructional strategies and materials which can be used as a beginning point for remediation. In the first and developmental phase of the project, descriptions of literacy events and environments are being written and functional literacy documents analyzed for their components. Examples of literacy events and situations being considered included interactions with environmental print (e.g., billboards, street signs, commercial marquees, storefront logos, etc.), functional literacy materials (e.g., newspapers, magazines, telephone books, bills, receipts, etc.) and employment related communication (i.e., job descriptions, application forms, technical manuals, memorandums, newsletters, etc.). Each of these situations and materials is being analyzed for its literacy requirements in terms of written display (i.e., knowledge of printed forms and conventions), meaning demands (i.e., comprehension and word knowledge) and social situation. From these descriptions, scripts are being generated which define the interaction between the student, an on-screen guide and the simulated literacy environment. The proposed outcome of the Evaluator is a diagnostic profile which will present broad classifications of literacy behaviors across the major areas of metacognitive abilities, word recognition, vocabulary knowledge, comprehension and writing. From these classifications, suggestions for materials and strategies for instruction with which to begin corrective action will be made. The focus of the Literacy Evaluator will be essentially to provide an expert diagnosis and an interpretation of that assessment which then can be used by a human tutor to further design and individualize a remedial program as needed through the use of an authoring system.

Yaden, David B., Jr.↗