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Record W2041916475 · doi:10.1016/j.egypro.2014.03.244

Potential of Concentrating Solar Power in Canada

2014· article· en· W2041916475 on OpenAlexafffundabout
Réda Djebbar, D Bélanger, E. Weterings, Michel Poirier

Bibliographic record

VenueEnergy Procedia · 2014
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsNatural Resources Canada
FundersGovernment of CanadaNatural Resources CanadaU.S. Department of Energy
KeywordsParabolic troughSolar ResourceSolar powerThermal energy storageResource (disambiguation)Concentrated solar powerEnvironmental scienceMeteorologySolar energyThermalEngineeringComputer sciencePower (physics)GeographyElectrical engineering

Abstract

fetched live from OpenAlex

In this paper, results of an analysis to assess the potential of concentrating solar thermal power applications in Canada are presented. First, a direct normal solar resource (DNI) resource map for Canada is introduced. This map indicates the locations where the DNI is the highest in Canada and is derived from the most recent Perez's SUNY satellite-based solar resource model Version number 3. Second, the methodology and results of a GIS analysis to identify the locations of the most suitable lands for concentrating solar thermal power (CSP) applications in Canada are discussed. The total areas of the CSP-suitable lands are presented in a tabulated and a map formats for each of the Canadian provinces where there is a maximum DNI solar resource. Third and finally, results of a technical economical analysis for two CSP system designs are discussed. The two CSP systems considered include parabolic trough with synthetic oil heat transfer fluid with and without storage, molten salt power tower with and without storage.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.004
GPT teacher head0.167
Teacher spread0.163 · 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

Citations36
Published2014
Admission routes3
Has abstractyes

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