Abundance estimates and habitat associations for two least-disturbed redside dace (<i>Clinostomus elongatus</i>) populations in tributaries of Lake Huron (Ontario, Canada)
Bibliographic record
Abstract
Abundance estimates are lacking for many imperilled fishes, which imposes uncertainty on species assessment and recovery processes. This limitation exists for redside dace ( Clinostomus elongatus), an Endangered species in Canada that has experienced extirpations due to widespread urbanization within its range. We developed N-mixture models to generate indices of abundance for two redside dace populations at the periphery of its Canadian range in an area unaffected by urbanization (Gully Creek and Unknown Stanley J (USJ) Tributary, Lake Huron, Ontario). Within-site heterogeneity in detection probability occurred, increasing between the first and second surveys at 88% of sites. Greater site abundance was observed in USJ Tributary than Gully Creek; extrapolating modelled site abundance estimates based on habitat availability resulted in median population abundance estimates above the minimum viable population size (28 606 adults in USJ Tributary (95% credibility interval: 21 727–35 593), 16 930 adults in Gully Creek (11 116–23 199)). Ultimately, these results will inform future habitat protection efforts and provide insight for the restoration of other populations within and beyond ecosystems affected by urbanization.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".