Factors Affecting the Detection of an Imperiled and Cryptic Species
Bibliographic record
Abstract
Population surveying and monitoring are important for identifying conservation needs and tracking trends in populations, communities, and ecosystems over time and laying the groundwork for conservation management and policy decisions. If species or populations go undetected because of inadequate effort or sampling design, protection and management cannot be properly provided. Due to the widespread loss of populations, the Eastern Massasauga (a rattlesnake) was recently listed as a federally threatened species in the United States; it is also listed as threatened in Canada. Given its current conservation status, there is considerable interest at state and federal levels in determining how to best survey for Eastern Massasaugas to aid in management decisions. Using a 16-year dataset, we examined the relationships among environmental, temporal, area, management, and search effort factors on the detection probability of Eastern Massasaugas. We found that four abiotic parameters (solar irradiance, shaded air temperature, three-day maximum air temperature, and humidity) and three search parameters (effort per researcher, search area, and search time of day) influenced detection of Eastern Massasaugas. As the current biodiversity crisis continues, the cost-effective use of resources and scientific expertise will continue to increase in importance. We hope our results stimulate similar analyses in other taxa, which will be critical for designing and implementing regional survey and monitoring programs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".