Testing the ability of habitat selection theory to predict interannual movement patterns of a drift‐feeding salmonid
Bibliographic record
Abstract
Abstract – This article extends the logic of a habitat selection model (Hughes, Ecology , 1998) to make predictions about the way body size will influence the probability that fish will make a long distance interannual movement, from the feeding position it occupies in one summer to the position it occupies the next. The model predicts that the probability of this kind of movement will fall as fish grow and reach zero for the largest fish in the population. I tested these predictions using data on Arctic grayling Thymallus arcticus in the lower 140 km of a 260‐km‐long interior Alaskan river. Both predictions of the model were well supported by the data. As expected, the probability a fish will make a long‐distance interannual movement decreased with fish size, and the largest fish in the population had a movement probability of zero. NOTE
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.005 | 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 teacher head, 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".