Factors Affecting Coastal Wetland Occupancy for Eastern Musk Turtles (Sternotherus odoratus) in Georgian Bay, Lake Huron
Bibliographic record
Abstract
In many jurisdictions, rare species and their habitats can receive protection if species are assessed as being at risk of declining. The assessment process requires data on habitat occupancy as well as identification of threats to a species critical habitat, both of which are difficult to obtain when the species occurs across large spatial scales. Such is the case for Eastern Musk Turtles (Sternotherus odoratus), which are obligate coastal wetland species in the Laurentian Great Lakes. We use data collected between 2003 and 2015 to map occupancy and conditional occupancy for musk turtles in coastal wetlands of eastern Georgian Bay (Lake Huron) to identify threats to resident wetland habitat. Data collected from a synoptic survey of 58 coastal wetlands were used to create occupancy models, estimate detection probability, and to conduct a sensitivity analysis to determine model robustness. We had a 64–71% probability of detecting musk turtles, whenever present in the wetland, and an area under curve value of 0.82 confirmed high model accuracy. Coastal wetlands that supported musk turtles were associated with higher proportions of forest cover, lower densities of roads, buildings, and docks within 1 km of the wetland, and more-variable bathymetric slopes. High conditional occupancy across the majority of our study area indicates that, at present, habitat in eastern Georgian Bay is in good condition; however, land-use alterations and development should be limited to ensure the persistence of this population of musk turtles.
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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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".