Do turtle roadkill hotspots shift from year to year?
Bibliographic record
Abstract
Freshwater turtles face many threats but roadkill is one of the most serious for many species. Roadkill of turtles is not uniformly distributed across roads but aggregated in certain areas, termed hotspots. A key question in identifying hotspots is whether they are fixed locations or if they shift from year to year because of changes in movement patterns. We compared how one, two, and three years of road survey data compared with the pooled data from four years of surveys. We found 254 turtles during 73 surveys during four years along a 15.5 km road section in Ottawa, Ontario, Canada. The four years of pooled data produced four hotspots (“pooled hotspots”) while each year or combination of years produced from three to five hotspots, four of which approximately corresponded to the pooled hotspots. The average percentage overlap of hotspots between one, two, or three years of survey data and the pooled hotspots ranged from 58.7% to 88.9%. Just one year of surveys sometimes missed one of the pooled hotspots, underestimated the spatial extent of the pooled hotspots, and also sometimes produced an additional “temporary” hotspot. Two years of surveys generally produced better approximations of the pooled hotspots and better identified the spatial extent of those hotspots.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.058 | 0.003 |
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; both teacher heads agree on what is shown here.
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".