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Record W1984049210 · doi:10.5539/mas.v6n6p45

Evaluation of Pads and Geometrical Shapes for Constructing Evaporative Cooling System

2012· article· en· W1984049210 on OpenAlexvenueno aff
S. I. Manuwa, Simon Ogbeche Odey

Bibliographic record

VenueModern Applied Science · 2012
Typearticle
Languageen
FieldEngineering
TopicAdsorption and Cooling Systems
Canadian institutionsnot available
Fundersnot available
KeywordsEvaporative coolerCharcoalMaterials scienceComposite materialHexagonal crystal systemSquare (algebra)MetallurgyMechanical engineeringMathematicsChemistry

Abstract

fetched live from OpenAlex

Investigations were carried out into local materials as cooling pads, and shapes for constructing evaporative coolers. Materials investigated include jute, latex foam, charcoal and wood shavings. Shapes of cooling systems considered were of hexagonal and square cross-sections. Some physical properties of pad materials that could affect the effectiveness of the evaporative coolers were also determined. Results of “No – load” tests carried out on the coolers indicated that the effectiveness of the cooling pads was in the following decreasing order of magnitude - Jute, latex foam, charcoal and wood shavings. The hexagonal shape cooler was found to be more efficient than the square shape. The average cooling or saturation efficiency for hexagonal cooler was 93.5% (jute), 91.4% (latex foam), 91.3% (charcoal) and 91.9% (wood shavings). The maximum temperatures observed were 6.4 (jute pad), 4.9 (latex foam pad), 5.2 (charcoal pad) and 3.6 degree Celsius. The results of this study will assist researchers in their selection of pad materials in the study of evaporative cooling systems.

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)0.002
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.047
GPT teacher head0.280
Teacher spread0.233 · 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

Citations32
Published2012
Admission routes1
Has abstractyes

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