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Record W3046155549 · doi:10.1080/02626667.2020.1798007

Mapping runoff generating areas using AGNPS-VSA model

2020· article· en· W3046155549 on OpenAlex
Kishor Panjabi, Ramesh Rudra, Pradeep Goel, Prasad Daggupati, Narayan Kumar Shrestha, Rituraj Shukla, Bin-Bin Zhang, Nabil Allataifeh

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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueHydrological Sciences Journal · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsMinistry of the Environment, Conservation and ParksUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSurface runoffEnvironmental scienceHydrology (agriculture)Nonpoint source pollutionInfiltration (HVAC)PollutantGeographyEcologyGeologyMeteorologyBiologyGeotechnical engineering

Abstract

fetched live from OpenAlex

In humid regions, surface runoff is often generated by saturation-excess runoff mechanisms from relatively small variable source areas (VSAs). However, the majority of the current hydrologic models are based on infiltration-excess mechanisms. In this study, the AGricultural Non-Point Source Pollution (AGNPS) model was used to integrate the VSA concept using topographic wetness index (TWI). Both the original and AGNPS-VSA models were evaluated for a small agricultural field in Ontario, Canada. The results indicate that the AGNPS-VSA model performed better than original model. The AGNPS-VSA model predicted that only the saturated portion of the field with higher TWI values produced runoff, whereas the original AGNPS model showed uniform hydrologic response from the entire field. The results of this study are important for accurately mapping the locations of VSAs. This new model could be a powerful tool in identifying critical source areas for applying targeted best management practices to minimize pollutant loads to receiving waters.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
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 score0.066
Threshold uncertainty score1.000

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 studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.082
GPT teacher head0.266
Teacher spread0.184 · 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