Resource selection functions for age-0 Arctic grayling (<i>Thymallus arcticus</i>) and their application to stream habitat compensation
Bibliographic record
Abstract
We developed resource selection functions (RSFs) for young-of-the-year (YOY) Arctic grayling (Thymallus arcticus) in a natural Barrenlands stream and used them to assess the habitat in an artificial stream created as part of a habitat compensation agreement in the Canadian Arctic. The model for small (1521 mm) grayling explained 55% of the variation in habitat use and included water velocity, average water depth, and percentage of detritus and fines. The model for large (3857 mm) grayling explained 36% of the variation in habitat use and included water depth, percentage of detritus and fines, and several cover variables. Model validation using a withheld sample of data indicated that the models provided good fits to the data, correctly classifying 7175% of habitat-use locations. Applying the RSFs to observed habitat use in the artificial stream indicated an abundance of quality habitat for small grayling, but a paucity for the larger YOY. These results reflect an ontogenetic shift in habitat requirements, from the simple needs of small YOY to the more complex demands of larger YOY, demands that could not be well met by the artificial stream. We suggest that this inability contributed to the poor productive capacity of the artificial stream.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".