Transferability of habitat preference criteria for larval European grayling (<i>Thymallus thymallus</i>)
Bibliographic record
Abstract
We examined the transferability of habitat preference criteria for larval European grayling (Thymallus thymallus) to two rivers in Finland by testing whether there were significant positive rank correlations between local fish densities (shoals per square metre) and preference indices for depth, velocity, substrate, and vegetation cover or selected combinations. Two sets of preference curves, one obtained from literature for the River Pollon, France, and another for the River Kuusinkijoki, Finland, were tested. All transferability tests for water velocity criteria were successful, correlation coefficients between local fish densities and preference indices ranging from 0.83 to 0.92. Criteria for depth and substrate transferred inconsistently, and criteria for vegetation cover failed to transfer to either target site. Combined indices predicted fish microdistributions inconsistently and they never performed better than the best univariate index for each site. Our results suggest that universal preference criteria for water velocity may exist for larval grayling and that it may be best to use these criteria alone in habitat hydraulic modelling when predicting habitat suitability to larval grayling.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".