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Record W2751857642 · doi:10.1063/1.5001402

A 1000x utility-scale parabolic frame tracker for multidisciplinary CPV research

2017· article· en· W2751857642 on OpenAlexafffund
Osvaldo Arenas, Richard S. Norman, Richard Prytula, Dominic Larkin, Fred de St-Croix, Sébastien Langlois, Vincent Aimez, Richard Arès, Luc G. Fréchette

Bibliographic record

VenueAIP conference proceedings · 2017
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsInstitut interdisciplinaire d'innovation technologiqueUniversité de Sherbrooke
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaNational Research FoundationUniversité de Sherbrooke
KeywordsFocus (optics)OpticsMaterials scienceScale (ratio)Computer scienceMechanical engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

A dual-dish concentrating solar research system is introduced in which multiple low-cost single-axis-focusing mirrors have their foci overlapped into a single intense compound focus. A CPV receiver for such a focus is also introduced, with cooled secondary mirrors and a Dense Receiver Array (DRA) with shingled cell rows to eliminate inter-row gaps. CTE-matched micro-channel cold plates are used for low-resistance cooling and fin tube radiators provide ample heat-rejection surface. The ratio of the DRA’s cell area to focusing mirrors’ area allows reaching a concentration factor of 1000x. A cost breakdown is presented and discussed and areas that still need significant improvement to be able to compete with flat panel costs are identified, along with research works in progress in those areas.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.012

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 head0.075
GPT teacher head0.337
Teacher spread0.262 · 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 designBench or experimental
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

Citations1
Published2017
Admission routes2
Has abstractyes

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