Suitability criteria analyzed at the spatial scale of redd clusters improved estimates of fall chinook salmon (<i>Oncorhynchus tshawytscha</i>) spawning habitat use in the Hanford Reach, Columbia River
Bibliographic record
Abstract
We improved our predictions of fall chinook salmon (Oncorhynchus tshawytscha) habitat use by analyzing spawning habitat at the spatial scale of redd clusters. Spatial point pattern analyses indicated that redd clusters in the Hanford Reach, Columbia River, were consistent in their location from 1994 to 1995. Redd densities were 16.1 and 8.9 redds·ha-1 in 1994 and 1995, respectively, and individual redds within clusters were usually less than 30 m apart. Pattern analysis also showed strong evidence that redds were uniformly distributed within the clusters where interredd distances ranged from 2 to 5 m. Redd clusters were found to occur predominantly where water velocity was between 1.4 and 2 m·s-1, water depth was 2-4 m, and lateral slope of the riverbed was less than 4%. This habitat use represented a narrower range of use than previously reported for adult fall chinook salmon. Logistic regression analysis determined that water velocity and lateral slope were the most significant predictors of redd cluster location over a range of river discharges. Overestimates of available spawning habitat lead to nonachievable goals for protecting and restoring critical salmonid habitat. Better predictions of spawning habitat may be possible if cluster-specific characteristics are used.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".