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Record W2071403337 · doi:10.1080/15435075.2013.773900

An Investigation of the Effect of Transparent Covers on the Performance of Cylindrical Solar Ponds

2013· article· en· W2071403337 on OpenAlexaff
İsmail Bozkurt, Ayhan Atız, Mehmet Karakılçık, İbrahim Dinçer

Bibliographic record

VenueInternational Journal of Green Energy · 2013
Typearticle
Languageen
FieldEnergy
TopicSolar-Powered Water Purification Methods
Canadian institutionsUniversity of Ontario Institute of Technology
Fundersnot available
KeywordsSolar pondSolar energyMicaThermalPolycarbonateThermal energy storageAbsorption (acoustics)SunlightEnvironmental scienceReflection (computer programming)Cover (algebra)Materials scienceEnvironmental engineeringOpticsMineralogyComposite materialMeteorologyGeologyEngineeringPhysicsElectrical engineeringMechanical engineeringThermodynamics

Abstract

fetched live from OpenAlex

The effect of the transparent covers (glass, polycarbonate, and mica) on the small cylindrical solar pond performance is studied. The temperature and density distributions are measured to evaluate the performance of the covered cylindrical solar pond. The pond covers provide a significant potential for energy savings and storage by insulating upper convective zone. Thus, such transparent covers are used for reducing the thermal energy losses from the top surface of the cylindrical solar pond. The transmission, reflection, and absorption coefficients of the covers are calculated to determine the monthly solar energy contents of the solar pond. The energy efficiencies of the solar pond are found for each type of covers from November 2008 to March 2009. As a result, the highest efficiency is determined to be 17.86% for glass cover in March, while the efficiencies of polycarbonate and mica become 16.95% and 15.86%, respectively. In this regard, the glass cover appears to be the best option for the solar ponds.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.272
Teacher spread0.253 · 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

Citations39
Published2013
Admission routes1
Has abstractyes

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