A Low-Cost, Efficient, and Precise Technique to Quantify Key Life Cycle Events in Nests of Oviparous Reptiles
Bibliographic record
Abstract
Phenological timing is of central interest to evolutionary ecologists because it is associated with fitness, but there has been limited study in animal groups with relatively secretive habits such as reptiles. This is especially true for the timing of hatchling behavior in wild reptile nests, likely attributable to few noninvasive methods for estimating parameters associated with egg hatching. We show that tri-axial accelerometers, small data loggers that measure rotation and inclination, can accurately quantify hatchling movement in wild reptile nests. In June 2018, we deployed an accelerometer in each of five freshly laid Snapping Turtle (Chelydra serpentina) nests in Algonquin Provincial Park, Ontario, Canada. In September 2018, nests were visited once daily to quantify the timing of hatchling emergence. The accelerometers worked as expected: there was statistically significant correspondence between the timing of accelerometer rotation in the nest (caused by movement of the hatchlings) and the timing of hatchling emergence. Furthermore, the number of hatchlings emerging from a nest was strongly and significantly correlated with the extent of accelerometer displacement. Our new technique allows new types of phenological data to be collected. It requires minimal effort and financial investment and thus is accessible to a broad range of research programs.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".