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Record W2972390529 · doi:10.1002/hyp.13875

Predicting evaporation from mountain streams

2020· article· en· W2972390529 on OpenAlexafffundabout
András Szeitz, R. Daniel Moore

Bibliographic record

VenueHydrological Processes · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRiparian zoneSTREAMSEvaporationEnvironmental scienceHydrology (agriculture)Atmospheric sciencesWind speedRange (aeronautics)MeteorologyGeologyGeographyMaterials scienceEcology

Abstract

fetched live from OpenAlex

Abstract Evaporation can be an important control on stream temperature, particularly in summer when it acts to limit daily maximum stream temperature. Evaporation from streams is usually modelled with the use of a wind function that includes empirically derived coefficients. A small number of studies derived wind functions for individual streams; the fitted parameters varied substantially among sites. In this study, stream evaporation and above‐stream meteorological conditions (at 0.5 and 1.5 m above the water surface) were measured at nine mountain streams in southwestern British Columbia, Canada, covering a range of stream widths, temperatures, and riparian vegetation. Evaporation was measured on 20 site‐days in total, at approximately hourly intervals, using nine floating evaporation pans distributed across the channels. The wind function was fit using mixed‐effects models to account for among‐stream variability in the parameters. The fixed‐effects parameters were tested using leave‐one‐site‐out cross‐validation. The model based on 0.5 m measurements provided improved model performance compared to that based on 1.5 m values, with RMSE of 0.0162 and 0.0187 mm h −1 , respectively, relative to a mean evaporation rate of 0.06 mm h −1 . Inclusion of atmospheric stability and canopy openness as predictors improved model performance when using the 1.5 m meteorological measurements, with minimal improvement when based on 0.5 m measurements. Of the wind functions reported in the literature, two performed reasonably while five others exhibited substantial bias.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.998

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)0.0030.001

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 head0.020
GPT teacher head0.217
Teacher spread0.198 · 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.

Study designObservational
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

Citations8
Published2020
Admission routes3
Has abstractyes

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