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Record W2014446906 · doi:10.1002/ird.1

Evaporation model of Lake Qaroun as influenced by lake salinity1

2001· article· en· W2014446906 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIrrigation and Drainage · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsEvaporationEnvironmental scienceHydrology (agriculture)RadiationPan evaporationEnergy budgetAtmospheric sciencesMeteorologyStandard deviationMathematicsStatisticsGeographyGeologyThermodynamicsPhysicsGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract A model for estimating monthly and annual evaporation of Lake Qaroun was developed using the energy budget concept. The evaporation is estimated from the amount of solar radiation, atmospheric long‐wave radiation, back radiation and evaporative energy. A modification is made to the energy equation to allow the calculation of evaporation from saline lakes by analyzing data from four evaporation pans with different salinities. The model simulations were checked against evaporation rates measured by Mankarous WF (1979. Hydrology of Lake Qaroun. MSc thesis, Faculty of Engineering, Cairo University, Cairo, Egypt). The monthly comparison shows that the model gives an acceptable accuracy with a relative error ranging from −12.4 to +12.9%. The model produces reliable results in terms of annual prediction with a maximum percent error of 3% and the minimum of −2.7%. The standard deviation of differences indicates that there is a good probability of obtaining simulated values within 0.8 mm (in May) to within 22.2 mm (in December) of measured values. Copyright © 2001 John Wiley & Sons, Ltd.

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.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.323

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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