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Record W4415912706 · doi:10.1002/eco.70121

Performance of Hydraulic Models Compared to Geomorphological and Botanical Approaches to Delineate the Ordinary High Water Mark for Small Rivers

2025· article· en· W4415912706 on OpenAlexaffabout
Freddy Houndekindo, Mathieu Vaillancourt, Sophie Duchesne, André St‐Hilaire, Monique Poulin, Charles Gignac, Normand Bergeron

Bibliographic record

VenueEcohydrology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Sediment Transport Processes
Canadian institutionsUniversité LavalInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsFloodplainFlood mythHydrology (agriculture)Vegetation (pathology)ShoreWatershedLand useRavine

Abstract

fetched live from OpenAlex

ABSTRACT The ordinary high water mark (OHWM) delineates the water environment from the land environment. The preferred method for the OHWM delineation in Québec, Canada, is a method based solely on bank vegetation characteristics, positioning the botanical OHWM (BOHWM) at the transition from hydrophytic to terrestrial vegetation. However, in anthropized landscapes, the implementation of this botanical method is problematic due to vegetation disturbance on the shoreline or riverbanks. The hydraulic and geomorphological methods position the OHWM at the 2‐year flood line and at the bankfull level of the river, respectively. The aim of this study was to compare the position of the 2‐year flood line to the BOHWM and to the geomorphological bankfull level for rivers located in agricultural, urban and forest areas. Results showed that the best concordance between the 2‐year flood line and the BOHWM was in agricultural areas (mean distance = 1.31 m) and the least in forest areas (mean distance = 3.31 m). The best agreement between the 2‐year flood line and the geomorphological method was found in agricultural (mean distance = 1.27 m) and urban (mean distance = 1.67 m) areas, while these two methods disagreed the most in forest areas (mean distance = 3.55 m).

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.363

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.047
GPT teacher head0.215
Teacher spread0.168 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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