Turning the threat into a solution: using roadways to survey cryptic species and to identify locations for conservation
Bibliographic record
Abstract
Freshwater turtles are one of the most imperilled groups of vertebrates globally, and roads have been associated with their decline. Although roads are typically viewed as an imminent threat to population persistence, because of direct mortality and increased landscape fragmentation, we argue that they are an important sampling tool for collecting a wide variety of data that can inform conservation efforts. Road surveys can yield important presence data when conducting species inventories, particularly for cryptic species, and can also indicate where to implement road mitigation measures. Our research examined three road survey methods from two previous studies (walking versus bicycling and walking versus driving) to test their relative effectiveness at locating turtles. We found that walking surveys yielded the highest number of turtles per kilometre; however, bicycling and driving surveys also presented advantages (specifically, the ability to survey longer lengths of road more quickly). We recommend using walking surveys in areas of specific interest (e.g. to investigate suitable habitat for imperilled species or to investigate the presence of cryptic species), and bicycling or driving surveys between sections of specific interest. Road survey methods could be used in addition to more traditional sampling approaches (e.g. trapping and visual surveys), and do not need to be restricted to areas where roadwork projects are in progress or being planned. Road surveys could also be used during general environmental assessments and ecological research, to effectively incorporate turtle presence data into conservation efforts.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.017 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".