Basin-scale availability of salmonid spawning gravel as influenced by channel type and hydraulic roughness in mountain catchments
Bibliographic record
Abstract
A general framework is presented for examining the effects of channel type and associated hydraulic roughness on salmonid spawning-gravel availability in mountain catchments. Digital elevation models are coupled with grain-size predictions to provide basin-scale assessments of the potential extent and spatial pattern of spawning gravels. To demonstrate both the model and the significance of hydraulic roughness, we present a scenario for optimizing the spatial extent of spawning gravels as a function of channel type in Pacific Northwest catchments. Predictions indicate that hydraulic roughness could control more than 65% of the potential available spawning habitat at our study sites. Results further indicate that bar roughness can be important for maintaining spawning gravels in lower mainstem reaches, while wood roughness may be required for spawning-gravel maintenance in steeper, upper mainstem channels. Our analysis indicates that wood loss and consequent textural coarsening could deplete up to one third of the potentially usable spawning area at our study sites.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".