Landscape composition predicts the local abundance of painted turtles ( Chrysemys picta )
Bibliographic record
Abstract
Urbanisation and agriculture are paradigmatic cases of habitat loss, degradation, and fragmentation that imperil wildlife. Anthropogenic landscape modifications can harm species such as freshwater turtles that rely on both aquatic and surrounding terrestrial habitats to survive and reproduce. We tested the hypothesis that the local abundance of painted turtles (Chrysemys picta) in wetlands depends on the composition of the surrounding landscape. We predicted that there would be fewer turtles in wetlands in more modified landscapes (i.e. urban and agricultural) with higher road densities. From repeated visual surveys of 34 wetlands around Ottawa, Canada we found that there were more painted turtles in wetlands that were larger and surrounded by more forest. Therefore, proper management of forested lands and green areas in urban landscapes are needed to protect turtles cohabiting with humans.
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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 | 0.000 | 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 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".