Fine-scale distribution and occupancy modelling of the threatened pugnose shiner (<i>Notropis anogenus</i>) in the St. Lawrence River, Ontario, Canada<sup>1</sup>
Bibliographic record
Abstract
Understanding population-level habitat requirements is important for the effective conservation of imperilled species, especially for those with fragmented distributions. This study examined fine-scale distribution of the threatened pugnose shiner (Notropis anogenus) in the upper St. Lawrence River, Ontario, Canada. Occupancy modelling, multivariate analyses, and co-occurrence modelling were used to identify environmental correlates of pugnose shiner distribution and species associations in an embayment area, Thompson’s Bay. The pugnose shiner was most abundant in outer bay sites that were cooler, less turbid, had a higher pH, and had more submerged aquatic vegetation than the inner bay sites. The probability of pugnose shiner occupancy increased with distance from the inner bay and with the presence of Chara vulgaris, and decreased with increasing conductivity. The pugnose shiner positively co-occurred with seven species, including the blackchin shiner and blacknose shiner, and negatively co-occurred with bluegill. Centrarchid species were dominant across Thompson’s Bay. This has important conservation implications because some native centrarchid predators are increasing in abundance, coincident with climate change, which may threaten the persistence of rare and imperilled cyprinids such as pugnose shiner.
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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 | 0.001 |
| 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 | 0.001 | 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".