Test of an environmental flow release in a British Columbia river: does more water mean more fish?
Bibliographic record
Abstract
1. Water managers must make difficult decisions about the allocation of streamflows between out-of-channel human uses and environmental flows for aquatic resources. However, the effects environmental flows on stream ecosystems are infrequently evaluated. 2. We used a 13-year experiment in the regulated Bridge River, British Columbia, Canada, to determine whether an environmental flow release designed to increase salmonid productivity was successful. A hierarchical Bayesian model was used to compare juvenile Pacific salmon (Oncorhynchus spp.) abundance before and after the flow release. 3. We found that the total number of salmonids did increase after the release, but most of the gains could be attributed to the rewatering of a previously dry channel located immediately below the dam. In reaches that had flowing water during the baseline period, the response of individual salmon species to the increase in flow was variable, and there was little change in total abundance after the flow release. Our results were inconsistent with both habitat modelling, which predicted a decrease in habitat quality with increasing flow, and holistic instream flow approaches, which imply greater benefits with larger flows. 4. We question whether biotic responses to flow changes can be predicted reliably with currently available methods and suggest that adaptive management or the use of decision tools that account for the uncertainty in the biotic response is required for instream flow decisions when the competing demands for water are great.
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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 | 0.002 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".