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Record W2608809907 · doi:10.14796/jwmm.r223-20

Calibration of BASINS HSP-F in Support of a Watershed Approach to CSO Long Term Control Planning

2005· article· en· W2608809907 on OpenAlexvenueno aff
Mary Perrelli, Kim Irvine, T.S. Lee

Bibliographic record

VenueJournal of Water Management Modeling · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsnot available
Fundersnot available
KeywordsWatershedNonpoint source pollutionTerm (time)Environmental scienceHydrology (agriculture)CalibrationWater resource managementComputer scienceEcologyMathematicsGeologyBiologyStatisticsWater qualityMachine learningPhysics

Abstract

fetched live from OpenAlex

UZSN and LZSN (upper and lower zone nominal soil moisture storage, respectively).Larger errors in model estimates for the years 1990, 1992, and 1995 frequently were traced to the rainfall data used to drive the runoff simulations.The rainfall record used in the calibration was for only one gauge site, the Buffalo Airport.Observation of weather radar and qualitative notation from field personnel indicated that considerable spatial variability occurred in rainfall patterns for the area.Validation runs were conducted for the year 2000 using only the Buffalo Airport rainfall data and spatially averaged rainfall data that also included two other rain gauges within the watershed.The validation run with the spatially averaged rainfall data had a higher r 2 and Nash Sutcliffe coefficient as compared to the validation run with the Buffalo Airport data alone.A lengthy time series of observed suspended solids data was not available to calibrate the sediment erosion and transport component of the model.However, turbidity data measured continuously at various sites along the Buffalo River in 2000 were available and relationships between suspended solids and turbidity were developed using least squares regression.For the purpose of model calibration the daily mean turbidity values were run through the appropriate regression equation to construct a suspended solids time series.Results of the calibration run for an example river reach that represents Cazenovia Creek, near the city line, and for the lower Buffalo River, near the Ohio St. bridge were evaluated in detail.Visually, the observed and modeled sediment time series for the two reaches corresponded, although quantitatively, the r 2 was low (in the range of 0.29).The model did a better job of representing erosion and transportation from the upper part of the watershed and had greater difficulty in representing the sediment deposition processes within the more hydraulically complex dredged channel (Ohio St. bridge site).

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)0.001
metaresearch head score (Gemma)0.004
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 score0.063
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.018
GPT teacher head0.234
Teacher spread0.216 · 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

Citations5
Published2005
Admission routes1
Has abstractyes

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