Demographic characteristics of Snapping Turtles (Chelydra serpentina) observed on roads in Ontario, Canada
Bibliographic record
Abstract
These data consist of 2 code files written in R and JAGS and three sets of observations of Snapping Turtles, <em>Chelydra serpenitina</em>, collected on roads in Ontario, Canada, which were assembled to undestand demographic characteristics associated with risk of road mortality in this species at-risk. The first data set is a sample of juvenile carcasses collected by us, or donated by colleagues, from five sites in central and eastern Ontario during 2013–2015. These carcasses were dissected to determine sex based on gonadal morphology, because juveniles lack external secondary sexual characters and sex in Snapping Turtles is not genetically determined. The second dataset records all alive- (AOR) and dead-on-road (DOR) Snapping Turtles observed in Presq'ile Provincial Park, Ontario, during systematic walking and bicycle surveys during 2013–2017. The third data set was collected in 2013–2015 and records all Snapping Turtles encountered on roads Algonquin Provincial Park, Ontario during field work associated with the Algonquin long-term Painted and Snapping Turtle life-history studies. The Algonquin dataset includes opportunistic observations collected outside of formal surveys.<br>Code files contain R and JAGS code for a somatic growth model and R code to construct and parameterize projection matrices for a set of stage structured demographic models.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.815 | 0.001 |
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 teacher head, 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".