Efficacy of Trail Cameras to Identify Individual Florida Panthers
Bibliographic record
Abstract
We conducted a 2-y investigation to assess the efficacy of trail cameras to identify individual Puma concolor coryi (Florida Panther). We established 35 camera sites within the 28,328-ha northern Addition Lands region of Big Cypress National Preserve from 1 January 2011 to 31 December 2012. To maximize the number of Florida Panthers captured, we intentionally avoided the use of transects or grids for camera-site selection. Instead, we placed cameras along known Florida Panther travel routes. We used a scent lure at each camera site to encourage Florida Panthers to linger in camera range, thereby increasing the opportunity to determine gender and observe anomalies that would aid in identification of individuals. Our cameras captured Florida Panthers 2154 times, which produced a total of 38,056 individual photos. We determined the identity of individual male Florida Panthers in 93% of captures (n = 1190 of 1278). However, the absence of anomalies in adult female Florida Panthers prevented us from identifying them consistently and with absolute certainty, despite thousands of opportunities to do so. Therefore, we relied on the morphological characteristics of dependent kittens to identify individual females in specific instances. We feel that the modifications to the camera survey (i.e., cameras placed on travel routes, high-quality digital cameras, and use of a species-specific scent lure) increased our ability to determine gender and identify individuals.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".