Using Patterns in Track‐Plate Footprints to Identify Individual Fishers
Bibliographic record
Abstract
Abstract: If individuals can be identified from patterns in their footprints, noninvasive survey methods can be used to estimate abundance. Track plates capture fine detail in the footprints of fishers ( Martes pennanti ), recording rows of dots corresponding to tiny papillae on the animal's metacarpal pad. We show that the pattern of these dots can be used to identify individual fishers, similar to human fingerprints. A probabilistic model of uniqueness based on variation in spacing between 1,400 pairs of dots that we measured in prints of 14 different fisher feet suggests the probability of encountering a similar pattern in the print of a different foot by chance alone is ≤ 0.35 n , where n = the number of dot pairs examined. This predicts a 0.00003 probability that a match made using 10 pairs of dots is false. Dot spacing from footprints made by the same foot was remarkably consistent (sN = 0.02 mm, n = 24 dot pairs). Combined, these results suggest dot patterns in fisher footprints were unique to individuals and were consistently reproduced on track plates. Empirical tests of matching accuracy were best with good‐quality prints, highlighting the need for experience judging when prints are usable. We applied print matching to fisher detections collected on track plates deployed at 500‐m intervals along 10 3.5‐km transects in the Adirondack region of New York, USA. Of 62 fisher detections, 85% had ≥ 1 footprint of suitable quality to compare with other high‐quality prints. We found that most detections from a transect were from the same individual fisher suggesting nonindependence of detections. Thus, data from traditional track‐plate deployments over small time periods cannot be used as a measure of abundance, but new study designs using print matching could obtain robust noninvasive, mark—recapture density estimates.
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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 | 0.002 | 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".