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Record W4210927286 · doi:10.29169/1927-5129.2022.18.02

Impact of Sand Filled Glass Bottles on Performance of Conventional Solar Still

2022· article· en· W4210927286 on OpenAlexvenueno aff
Pankaj Dumka, Harshit Gautam, Saksham Sharma, Chinmay Gunawat, Dhananjay R. Mishra

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2022
Typearticle
Languageen
FieldEnergy
TopicSolar-Powered Water Purification Methods
Canadian institutionsnot available
Fundersnot available
KeywordsBottleExergy efficiencyMaterials scienceEnvironmental scienceSolar energyComposite materialPulp and paper industryWaste managementExergyEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Experimental evaluation of conventional solar still (CSS) and CSS with sand-filled glass bottles (Modified solar still (MSS)) have been reported in this article. Two identical CSS were fabricated for the experiments. The experiments were designed and performed under Guna (India) weather conditions in November 2021. Sand-filled glass bottles were paced as sensible energy-storing material. The mathematical model proposed by Kumar and Tiwari has been utilized to evaluate internal heat transfers, internal efficiency, and exergy efficiency. For checking the economic feasibility of sand-filled glass bottle within CSS, a comparative cost analysis has been performed to evaluate CPL (Cost per Litre). It has been observed that the MSS has yielded 21.32% more than CSS. And overall internal efficiency has also increased by 139.45% due to the presence of sand-filled glass bottles.

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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.032
GPT teacher head0.308
Teacher spread0.276 · 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

Citations7
Published2022
Admission routes1
Has abstractyes

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