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Record W4285403516 · doi:10.1016/j.renene.2022.07.011

Impact of measured spectrum variation on solar photovoltaic efficiencies worldwide

2022· article· en· W4285403516 on OpenAlexaff
Geoffrey S. Kinsey, Nicholas Riedel, Alonso-Abella Miguel, Matthew Boyd, Marília Braga, Chunhui Shou, Raúl R. Cordero, Benjamin C. Duck, Christopher J. Fell, Sarah Féron, George E. Georghiou, Nicholas Habryl, Jim Joseph John, Nipon Ketjoy, Gabriel López, Atse Louwen, Elijah Loyiso Maweza, Takashi Minemoto, Ankit Mittal, Cécile Molto, Guilherme Neves, G. Nofuentes, Matthew Norton, Basant Raj Paudyal, Ênio Bueno Pereira, Yves Poissant, Lawrence E Pratt, Shen Qu, Thomas Reindl, Marcus Rennhofer, Carlos D. Rodríguez‐Gallegos, Ricardo Rüther, Wilfried van Sark, Miguel A. Sevillano-Bendezú, Hubert Seigneur, Jorge A. Tejero, Marios Theristis, Jan Amaru Töfflinger, Carolin Ulbrich, Waldeir Amaral Vilela, Xiangao Xia, Márcia Akemi Yamasoe

Bibliographic record

VenueRenewable Energy · 2022
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsNatural Resources Canada
FundersNational Renewable Energy LaboratorySandia National LaboratoriesJoint Research CentreNational Research Foundation SingaporeNational Institute of Standards and TechnologyUniversity of OregonConsejo Nacional de Ciencia, Tecnología e Innovación TecnológicaAgência Nacional de Energia ElétricaNational University of SingaporeNational Research FoundationEuropean Regional Development FundU.S. Department of EnergyEuropean CommissionEnergy Market Authority of Singapore
KeywordsIrradiancePhotovoltaic systemEnvironmental scienceSolar irradianceSolar energyBroadbandSolar cellVariation (astronomy)SeasonalityAtmospheric sciencesRemote sensingMeteorologyOpticsMaterials sciencePhysicsStatisticsOptoelectronicsGeographyEngineeringMathematicsAstrophysicsElectrical engineering

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.193
Teacher spread0.186 · 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 designObservational
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

Citations47
Published2022
Admission routes1
Has abstractno

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