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Record W2063548100 · doi:10.1109/igarss.2006.113

Supporting Energy-Related Societal Applications Using NASAs Satellite and Modeling Data

2006· article· en· W2063548100 on OpenAlexfundaboutno aff
Paul W. Stackhouse, Richard S. Eckman, T. Zhang, J. Colleen Mikovitz, C. H. Whitlock, William S. Chandler, J. M. Hoell, Gregory J. Leng, Peter Lilienthal

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicSolar Radiation and Photovoltaics
Canadian institutionsnot available
FundersScience Mission DirectorateNational Renewable Energy LaboratoryNatural Resources CanadaNational Aeronautics and Space Administration
KeywordsRenewable energySatelliteSolar energyWork (physics)Resource (disambiguation)Natural resourceSolar powerEnvironmental scienceEnergy managementResource management (computing)Remote sensingComputer scienceSystems engineeringMeteorologyEnergy (signal processing)EngineeringPower (physics)Aerospace engineeringGeography

Abstract

fetched live from OpenAlex

Improvements to NASA Surface Meteorology and Solar Energy (SSE) web site are now being made through the Prediction of Worldwide Energy Resource (POWER) project under NASA Science Mission Directorate Applied Science Energy Management Program. The purpose of this project is to tailor NASA Science Mission results for energy sector applications and decision support systems. The current status of SSE and research towards upgrading estimates of total, direct and diffuse solar irradiance from NASA satellite measurements and analysis are discussed. Part of this work involves collaborating with partners such as the National Renewable Energy Laboratory (NREL) and the Natural Resources Canada (NRCan). Energy Management and POWER plans including historic, near-term and forecast datasets are also overviewed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.036
Threshold uncertainty score0.071

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0110.004

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.049
GPT teacher head0.309
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2006
Admission routes2
Has abstractyes

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