Maximum stream temperature and the occurrence, abundance, and behavior of steelhead trout (<i>Oncorhynchus mykiss</i>) in a southern California stream
Bibliographic record
Abstract
To determine individual and population responses of steelhead trout (Oncorhynchus mykiss) to stream temperature, we sampled summer stream temperature and juvenile steelhead occurrence, abundance, and behavior in a small stream near the species' southern limit. Maximum annual stream temperature (22.3–33.1 °C) exerted a strong threshold effect on steelhead occurrence. Steelhead persisted through summer in all pools that did not exceed 30 °C, but the probability of persistence rapidly decreased to zero at warmer sites. Below the threshold for fish persistence, thermal effects on steelhead abundance were inconsistent, with temperature receiving strong support for inclusion in models of fish abundance at the beginning of summer but weak support for inclusion in models of end-of-summer abundance and summer fish loss. Thermal refugia that would allow steelhead to behaviorally thermoregulate were rare. In response to elevated stream temperatures, steelhead reduced foraging and agonistic activity, presumably to minimize energetic costs. In anticipation of the effects of global climate trends on stream temperature, management actions that limit or reduce stream heating will be critical for conservation of steelhead populations in habitats approaching thermal tolerance thresholds.
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 | 0.000 | 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 | 0.000 | 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".