Relationship between Habitat Quality and Occurrence of the Threatened Black Redhorse
Bibliographic record
Abstract
Recovery planning for the nationally threatened black redhorse (Moxostoma duquesnei) is limited by a lack of knowledge regarding species ecology, population size and factors that affect distribution and abundance. Generalized additive models (GAM) were used to evaluate the influence of habitat quality on the distribution of the black redhorse in the Grand River (Ontario) and 11 western Lake Erie tributaries (Ohio). Black redhorse were captured at 26 % of Grand River sites and 6 % of western Lake Erie tributary sites. In western Lake Erie tributaries, black redhorse were more likely to be found at sites of intermediate upstream drainage area (a surrogate for watercourse size) and less likely to be found at sites with poor sub-strate, pool, cover and channel conditions. In the Grand River, occurrence was negatively associated with higher gradients and small and large upstream drainage areas. Habitat quality was found to be associated with the distribution of golden red-horse (M. erythrurum) but not the other two co-occurring redhorse species. Site occupancy was negatively associated with poor substrate and pool conditions. Results from this study indicate that, in areas of black redhorse occurrence, river reaches with clean, coarse bed material, well-developed riffles and pools, and stable channels require specific protection. Repatria-tion efforts in formerly occupied watercourses will likely require restoration of the condition of these habitats.
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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 | 0.002 |
| 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) | 0.002 | 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".