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Record W1980966876 · doi:10.1139/s06-029

Testing and application of a two-dimensional hydrothermal model for a water supply reservoir: implications of sedimentation

2007· article· en· W1980966876 on OpenAlexvenueno aff
Rakesh K. Gelda, Steven W. Effler

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsStratification (seeds)EpilimnionSedimentationThermal stratificationHydrothermal circulationHydrology (agriculture)GeologyDrawdown (hydrology)Environmental scienceSedimentOceanographyHypolimnionThermoclineGeomorphologyEcologyAquiferGeotechnical engineeringGroundwaterNutrient

Abstract

fetched live from OpenAlex

Validation of a two-dimensional hydrothermal stratification model for 14 years for Schoharie Reservoir, N.Y., an impoundment that experiences substantial drawdown, is documented. The model is demonstrated to perform well in simulating the various features of the reservoir's thermal stratification regime in each of the years, including the timing and duration of stratification, the dimensions and temperature of the layers, and the periods of internal wave oscillations in stratified layers. The average root mean square error for temperature predictions for the 14 years is 1.30 ºC. The validated model is applied in a probabilistic manner to investigate the impacts of sedimentation on the temperature of the withdrawn water (T w ) that enters a stream with a salmonid fishery, and features of the stratification regime. The predicted effects of sedimentation include a shift toward higher T w values, a shortening of the duration of stratification, and decrease in the depth of the epilimnion in fall. The effects on T w are exacerbated for the reservoir because of localized sedimentation adjoining the intake that increasingly limits access to cooler layers.Key words: hydrothermal model, temperature, model, reservoir, water supply, model projections, sedimentation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.179

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 communication0.0000.000
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 head0.220
Teacher spread0.212 · 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

Citations24
Published2007
Admission routes1
Has abstractyes

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