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Record W198231949

Hydrologic modelling of the Canard River watershed

2007· article· en· W198231949 on OpenAlexaboutno aff
Md. Masihur Rahman

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2007
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWatershedHydrology (agriculture)Environmental scienceHydrological modellingGeologyClimatologyComputer science
DOInot available

Abstract

fetched live from OpenAlex

Soil and Water Assessment Tool (SWAT), a continuous simulation model was used to conduct hydrologic analysis, and to predict sediment and nutrient loadings from the Canard River watershed to the Detroit River. The watershed is located within the Essex Region in southern Ontario, and it has been identified as a part of the Area of Concern of the Detroit River for non-point source pollution. The SWAT model was calibrated and tested using daily observed streamflow data. The Nash-Suttcliffe model efficiencies for monthly streamflow predictions were 0.79 and 0.85, respectively during the calibration and testing periods. It was found from the model that on average, 36% of the annual precipitation over the watershed contributed to the streamflow during the five-year period 2001-2005. The northeastern part of the watershed was found to be yielding higher sediment and nutrient loads. Hence these areas are of critical importance for the watershed management.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.496
Threshold uncertainty score0.986

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.193
Teacher spread0.171 · 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 designSimulation or modeling
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

Citations3
Published2007
Admission routes1
Has abstractyes

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