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Record W2789797850 · doi:10.1002/eco.1959

Effect of discharge and habitat type on the occurrence and severity of <scp><i>Didymosphenia geminata</i></scp> mats in the Restigouche River, eastern Canada

2018· article· en· W2789797850 on OpenAlexaffabout
Carole‐Anne Gillis, Stephen J. Dugdale, Normand Bergeron

Bibliographic record

VenueEcohydrology · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicFish Ecology and Management Studies
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsEnvironmental scienceDiatomWatershedHabitatEcosystemHydrology (agriculture)EcologyFisheryBiologyGeology

Abstract

fetched live from OpenAlex

Abstract Since 2006, the Restigouche River watershed, eastern Canada, has been affected by nuisance growths of the mat‐forming diatom, Didymosphenia geminata . In 2010, in view of the potential impacts of this alga on the local Atlantic salmon fishery, we created a volunteer monitoring network to assess D . geminata mat severity within the watershed. Over the course of 6 monitoring summers, more than 1,200 observations of D . geminata mat severity were reported in 20 subwatersheds of the Restigouche River basin. Observations were mapped to illustrate the yearly severity of D . geminata mats throughout the watershed. Metrics were then extracted from this dataset to assess the spatial and temporal variability of mat severity. At the reach scale, D . geminata occurrence was predominantly found in riffles compared to any other river habitat type. At the watershed scale, a two‐sample Kolmogorov–Smirnov test highlighted a significant effect of maximum spring discharge on mean annual D . geminata mat severity, indicating that when maximum spring discharge is high, severity of D . geminata mats in the following months is significantly lower. Additionally, maximum spring discharge explained 71% of the variability in annual mat severity. This study contributes to the understanding of mat severity dynamics and illustrates the value of volunteer monitoring networks for studying complex ecosystem dynamics.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.993

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 studies0.0000.001
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 head0.005
GPT teacher head0.206
Teacher spread0.201 · 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 designObservational
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

Citations10
Published2018
Admission routes2
Has abstractyes

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