Implications of legacy watershed disturbances for channel structure and salmon habitat availability under different low-flow levels: an analysis of 45 years of discharge–habitat relationships at Carnation Creek, British Columbia
Bibliographic record
Abstract
In streams where water availability is limited, conservative flow ranges are often adopted by water managers to ensure that streamflow is available to meet the ecological requirements of aquatic organisms. However, a variety of natural and anthropogenic disturbances can influence stream channel morphology and in-stream wood characteristics through time, potentially altering the availability of habitat at a given flow level. Using a 2D hydrodynamic modelling approach incorporating 45 years of detailed channel morphology data from Carnation Creek, British Columbia, this paper examines relationships between legacy (forestry-driven) watershed disturbance, changes to channel morphology, and habitat availability for juvenile coho salmon (Oncorhynchus kisutch) under nine flow levels. Results indicate that substantial variability in the abundance of salmonid habitat is present through time, even when modelled flow levels are held constant. Additionally, trade-offs were observed between availability of habitat types as discharge increased. Finally, modelling results indicate that habitat availability is reduced following historical harvesting. These findings suggest that legacy watershed disturbances affecting stream channel form and function are worth considering when allocating streamflow.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".