Ecological correlates of fish movement in a network of Virginia streams
Bibliographic record
Abstract
Identifying factors that influence fish movement is a key step in predicting how populations respond to environmental change. Using markrecapture (four species) and trap capture (eight species) data, we examined relationships between three attributes of movement and 15 ecological variables. The probability of emigrating from a reach was positively related to intermittency (one species) and body size (one species) and negatively related to distance from the mainstem creek (two species) and habitat complexity (one species). The number of fish moving upstream through traps was positively related to increases in flow (five species), day length (three species), and water temperature (two species); the number moving through downstream traps was positively associated with increases in flow (three species). Distance moved was greater for fish moving through unsuitable reaches (one species). Floods have a pervasive effect on fish movement, and human activities that affect flows will have widespread implications. The importance of other factors varies interspecifically, which may translate into variation in persistence and colonization rates. For example, species that exhibit reach fidelity in complex habitats may increase movement if habitats are homogenized. These species may suffer population declines because of the cost of increased movement and may ultimately be replaced by ecological generalists.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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".