The impact of urbanization on painted turtle (<i>Chrysemys picta</i>) behaviour
Bibliographic record
Abstract
Abstract Urbanization is a significant driver of the global biodiversity crisis. Turtles are particularly impacted by urbanization because of the vulnerability of riparian habitats to habitat loss and road mortality. Behaviour plays a crucial role in determining the success of urban animals. Behavioural responses to urbanization, however, are rarely studied in turtles even though many turtles are at‐risk and sometimes live in urban areas. Therefore, we evaluated behavioural changes in painted turtles (Chrysemys picta) living in wetlands surrounded by a gradient of urbanization. We tested the consistency of painted turtle behaviour in the laboratory and examined the behaviour of painted turtles from 24 wetland sites across an urbanization gradient in Ottawa, Ontario, Canada. We assessed: (i) aggression by measuring the number of active defensive behaviours the turtles performed in response to handling, (ii) boldness by measuring the amount of time the turtles took to emerge from their shells and move from their initial locations in a circular arena and (iii) activity by measuring the amount of time the turtles spent moving in the same circular arena. We found that all behaviours were consistent in the laboratory. We also found that as the level of urbanization increased, turtles were more aggressive and bolder. Urbanization affects painted turtle behaviour, but further research is required to understand the mechanisms responsible and the conservation implications.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".