Habitat-specific biomass, survival, and growth of rainbow trout (<i>Oncorhynchus mykiss</i>) during summer in a small coastal stream
Bibliographic record
Abstract
We observed significant habitat-scale variation in the density, survival, and growth of 811 passive integrated transponder tagged rainbow trout (Oncorhynchus mykiss) enclosed for 6368 days at natural density in 59 individual habitats (pools and riffles) in a small coastal California stream in summer 2001. The initial habitat-scale densities of trout were positively related to both depth and cover, although by the end of the experiment, the influence of cover on density was less apparent. Survival varied substantially, but with no clear relationship to any of the physical factors that we measured. Mean growth was density dependent, positively influenced by habitat depth, and also increased downstream within the 2-km study reach. The overall change in trout biomass over the experiment was also negatively related to fish density and positively influenced by habitat depth. Individual growth within habitats appeared to be unrelated to body size. The positive influences of water depth and distance downstream on trout growth in this experiment suggest that management actions that increase dry season discharge and limit aggradation will benefit salmonids in small streams.
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 | 0.000 |
| 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) | 0.000 | 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".