Occupancy and detection of Wavyrayed Lampmussel (<i>Lampsilis fasciola</i>) in Ontario, Canada<sup>1</sup>
Bibliographic record
Abstract
Freshwater mussels (Bivalvia: Unionidae) are the most imperilled taxon in Canada. To facilitate species recovery efforts, an understanding of species distribution and habitat that supports species persistence is needed. Detecting mussels presents unique challenges and requires considerable effort owing to their complex life histories and widespread declines. Here, observations of the imperilled Wavyrayed Lampmussel (Lampsilis fasciola Rafinesque, 1820) from the Grand and Thames rivers, Ontario, Canada, were used to quantify species detection and occupancy probabilities, and the relationship between occupancy probability and substrate size. The best model for the data included a river-specific covariate for detection and an intercept model for occupancy. Detection probability of Wavyrayed Lampmussel was higher in the Grand River than in the Thames River. Limited variation in substrate size measurements restricted occupancy modelling, but field measurements qualitatively aligned with previous habitat descriptions. Overall, knowledge of species detection and occupancy probabilities for Wavyrayed Lampmussel will not only enhance the understanding of species distribution and habitat associations, but also ensure that the response of the species to threats and recovery actions are captured.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".