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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".