Survey methodology for the detection of Wood Turtles (<em>Glyptemys insculpta</em>)
Bibliographic record
Abstract
Wood Turtles (Glyptemys insculpta) are difficult to survey because their use of aquatic and terrestrial environments varies spatio-temporally. Existing survey methodology is highly variable and typically involves searching for Wood Turtles within water and on land 0 to >20 m from the shoreline from spring to autumn. The mobility of Wood Turtles suggests that detection is likely influenced by distance surveyed from water and the amount of vegetation, which varies by season. To determine an ideal survey methodology for the Wood Turtle, we recorded distances from a waterway of 31 radio-tagged turtles at Canadian Forces Base, Gagetown, New Brunswick, in 2003 and 2004. Ordinal logistic regression was used to determine the probability of finding male or female Wood Turtles with increasing distance from water at different times of day or season. Sex and time of day were not significant factors in detecting Wood Turtles. Season was a significant factor, with highest probability (69%) of finding Wood Turtles at a distance of 0–10 m of a waterway up to July 1 (corresponding to pre-nesting and nesting periods), compared to probabilities of <10% for any 10-m distance between 10 m and 50 m from a waterway. After July 1, the highest detection probability (50%) was at distances greater than 50 m from a waterway. We recommend that Wood Turtle surveys for environmental impact assessments and population monitoring be conducted on warm days (i.e., 10–25°C) within 10 m of waterways up to July 1.
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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 | 0.001 | 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 | 0.001 | 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) | 0.002 | 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".