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Record W34184993 · doi:10.2196/24353

CONTROL DESIGN PROFITS HELIOSTAT TECHNO-ECONOMICS

2012· article· en· W34184993 on OpenAlexvenueno aff
Nava Rubin, Thomas H. Roos, Fintan Wilson, Jason de Villiers, Strinivasan Perumal

Bibliographic record

VenueJMIR Formative Research · 2012
Typearticle
Languageen
FieldEngineering
TopicSpacecraft Design and Technology
Canadian institutionsnot available
FundersNational Institute on Drug AbuseNational Institute on Alcohol Abuse and Alcoholism
KeywordsHeliostatRenewable energyIdentification (biology)Process (computing)Solar energyComputer scienceControl engineeringControl (management)Product (mathematics)EngineeringProcess engineeringSystems engineeringArtificial intelligenceMathematicsElectrical engineering

Abstract

fetched live from OpenAlex

Modelling, Identification, Simulation and Synthesis are well known components of the System Engineering Process. Ally these with the Target Aligned (TA) heliostat architecture and the universally acknowledged merits of using closed loop feedback control, then one has all the requisite tools to produce a cost effective solar energy product that can deliver impact in the renewable energy arena.

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)0.013
metaresearch head score (Gemma)0.038
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 score0.112
Threshold uncertainty score0.374

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.038
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.001
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.1120.010

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 head0.053
GPT teacher head0.332
Teacher spread0.278 · 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

Citations0
Published2012
Admission routes1
Has abstractyes

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