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Record W4401332364 · doi:10.1080/02626667.2024.2387155

Estimation of the time of concentration of small watersheds located in Northeastern North America

2024· article· en· W4401332364 on OpenAlexaffabout
Samuel Bolduc, Alain Mailhot, Guillaume Talbot

Bibliographic record

VenueHydrological Sciences Journal · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsHydrology (agriculture)Environmental scienceStreamflowPrecipitationEstimationRegressionWetlandPhysical geographyGeographyStatisticsEcologyDrainage basinMathematicsGeologyMeteorologyCartographyBiologyEngineering

Abstract

fetched live from OpenAlex

The time of concentration is an important concept in hydrology. It provides a characteristic hydrological response time (CHRT) useful in many applications. Estimation of the time of concentration is challenging because small watersheds (<100 km2) with sub-daily flow and precipitation records are uncommon. Many practitioners therefore use empirical equations developed from watersheds exposed to different climates and with different attributes. The main objective of this study is to develop an approach to estimate the CHRT from physiographic characteristics for small watersheds located in Ontario, Québec and the northeastern USA. Regression trees are used to identify the physiographic characteristics associated with CHRT. The fraction of lakes and wetlands was identified as the most significant attribute related to CHRT, followed by the ratio between the main watercourse length and the square root of the main watercourse slope. Uncertainties on estimated CHRT values based on regression tree are also provided.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.109
Threshold uncertainty score0.419

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.015
GPT teacher head0.224
Teacher spread0.210 · 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 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

Citations1
Published2024
Admission routes2
Has abstractyes

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