Effectiveness of an ecological flow regime to assure successful recruitment of anadromous Coregoninae populations in the Rupert River (northern Quebec, Canada)
Bibliographic record
Abstract
Abstract Objective As part of a large-scale hydroelectric project, the mean annual flow of the Rupert River (northern Quebec, Canada) was reduced by 52% at its mouth in 2009. To protect fish habitat, an ecological flow regime that was modulated according to the biological seasons was implemented downstream from the diversion point. An 8-year monitoring program, including 2 years before partial diversion, was carried out to verify the effectiveness of this mitigation measure on the total annual abundance of anadromous Coregoninae (Cisco Coregonus artedi and Lake Whitefish C. clupeaformis) larvae, which was used as biological indicator of recruitment success. The monitoring also aimed to determine the effects of flow modification on the timing of the larval drift and the spatial distribution of larvae in a river cross section. Methods Each year, sampling consisted of installing drift nets during the entire downstream larval migration in a river cross section of the lower Rupert River. Drift nets were systematically placed to ensure representative sampling of the river section. Result Prior to flow reduction, the estimated total number of larvae varied between 1.8 and 8.6 million. Over the following 6 years, the estimated larval population has remained steady at 3–4 million. Otherwise, larval drift characteristics have not changed since the flow reduction, as (1) the duration of the larval drift is the same as before, about 1 month in May and early June, with a peak period of about 8 days; (2) rise in water temperature in spring is a determining factor in the timing of larval drift; and (3) the larvae drift mainly near the surface of the water. Conclusion The monitoring results indicate that the ecological flow regime implemented in the Rupert River was adequate to maintain anadromous coregonine populations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".