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Record W2343622671 · doi:10.4236/ojce.2016.63029

Evaporation Retardation by Monomolecular Layers: An Experimental Study at the Aji Reservoir (India)

2016· article· en· W2343622671 on OpenAlexaff
Kishor Panjabi, Ramesh Rudra, Pradeep Goel

Bibliographic record

VenueOpen Journal of Civil Engineering · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsMinistry of the Environment, Conservation and ParksUniversity of Guelph
Fundersnot available
KeywordsEvaporationAridEnvironmental scienceSurface waterSurface runoffEnvironmental engineeringHydrology (agriculture)GeologyGeotechnical engineeringMeteorologyGeographyEcology

Abstract

fetched live from OpenAlex

It is an established fact that huge quantities of water are lost from lakes, reservoirs and soils by evaporation. This assumes greater significance in arid and semi-arid regions around the globe when a general scarcity of water is compounded by high evaporation loss from the open water surfaces of lakes and reservoirs. The use of surface covering by a monomolecular film to reduce evaporation loss from large open water surfaces offers the greatest promise among all currently available techniques. This is the only system that retains the water surface in a state that does not interfere with other uses of the body of water such as boating, navigation recreation, fish, and wildlife propagation. Various experiments and field trials worldwide have proven conclusively that the fatty alcohols and their emulsions effectively retard water evaporation and result in saving to the tune of about 20% to 50%. An experiment was carried out at the Aji Reservoir (India) using a mixture of Cetyl and Stearyl alcohol that confirmed 19.26% saving in evaporation loss. During this six-month trial, about 0.18 mcum of water was saved which otherwise might have evaporated.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.358
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

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

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 head0.011
GPT teacher head0.229
Teacher spread0.219 · 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 teacher head, 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

Citations17
Published2016
Admission routes1
Has abstractyes

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