Landscape determinants of aquatic estuarine habitat use by juvenile Chinook salmon
Bibliographic record
Abstract
Habitat restoration planning and design can be informed by information on spatial patterns in habitat use, which can be obtained from temporally and spatially extensive monitoring efforts. Using records from long-term monitoring in four tidal deltas, we modeled how landscape features (channel types, wetland vegetation types, and a channel network connectivity index) influenced juvenile natural-origin (NOr) and hatchery-origin (HOr) Chinook salmon presence/absence and density when present. Models detected strong effects of landscape characteristics on either presence/absence or density, although the directionality and relative importance of these features differed between NOr and HOr fish. In addition, while interannual differences were stronger for NOr juveniles, the seasonal pattern was much stronger for HOr fish and was in fact the strongest predictor of density. For both types of fish, interactions between landscape features and seasonal pattern existed, indicating that habitat use changed dynamically from the beginning to the end of the rearing period. These findings help provide general principles for prioritizing location and designing habitat restoration projects.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".