Evaluation of headstarting as a conservation tool to recover Blanding’s Turtles (Emydoidea blandingii) in a highly fragmented urban landscape
Bibliographic record
Abstract
Freshwater turtle populations are declining globally as a result of anthropogenic activities. Threats to turtles in urban areas are exacerbated by road mortality and subsidized predators, which can lead to catastrophic shifts in population size and structure. Headstarting is used as a conservation tool to supplement turtle populations that may otherwise face extirpation. A headstarting program began in 2012 to recover a functionally extinct population of Blanding's Turtles (Emydoidea blandingii) 26in Rouge National Urban Park (RNUP), Ontario, Canada. The original population included five adults and one juvenile turtle. From 2014 to 2020, 270 headstarted turtles were released. The population has been monitored annually since 2014 using visual-encounter surveys, radio-telemetry, and live trapping (from 2018 onwards). We used mark-recapture and radio-telemetry data to quantify abundance, survival, and sex ratio of the headstarted turtle population. Using a Jolly-Seber model, we estimated abundance to be 183 turtles (20 turtles/ha) in 2020. Estimated survival of headstarted turtles approached 89%, except for turtles released in 2019 when survival was 43% as a result of a known mass mortality event at the study site. Pre- and post-release sex ratios were not significantly different (χ2 = 1.92; p = 0.16), but shifted from 1:1.5 to 1:1 male:female post-release. Given that the headstarted turtles have not yet reached sexual maturity, it is unclear whether headstarted turtles will reach adulthood and successfully reproduce to maintain a self-sustaining population. Thus, to evaluate the success of the headstarting program, long-term monitoring is required.
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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 | 0.002 | 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".