Using decades of spawning data and hydraulic models to construct a temperature-dependent resource selection function for management of an endangered salmonid
Bibliographic record
Abstract
In North America, impassable, man-made barriers block access to salmonid spawning habitat and require costly restoration efforts in the remaining habitats. Evaluating restored spawning habitat quality requires information on salmon water velocity and depth preferences, which may vary in relation to other variables (e.g., water temperature). We demonstrate a generalizable, low-cost method to gather and analyze these data by combining aerial redd surveys of winter-run Chinook salmon (Oncorhynchus tshawytscha), 2D hydraulic modeling, and generalized linear models to calculate spawning resource selection functions (RSFs). Our method permits the examination of interactions between environmental variables and habitat selection, which are frequently treated as independent. Our methods resulted in an RSF that shows interactions between both velocity and depth preference with changing temperature. Preferred depth increased and preferred velocity decreased with increasing temperature. Spawning RSFs for environmental variables may change as other environmental conditions (i.e., water temperature) change; thus, it is important to account for potential interactions when using or producing RSFs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 |
| 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".