The individual color pattern on the back of <i>Bufotes viridis balearicus</i> (Boettger, 1880) allows individual photo-identification recognition for population studies
Bibliographic record
Abstract
This study explores the potential of photo-identification methods (PIMs) as a viable, non-invasive, and ethical tool for wildlife studies, with a specific focus on anuran species such as Bufotes viridis balearicus (Boettger, 1880). Although the automatic photo-identification suite (APHIS) software was initially designed for lizard identification, our research shows its adaptability for anuran species, achieving a high detection accuracy rate of 95.28%, thus obtaining outstanding and higher values in compared to previous studies on this species. Crucially, our findings indicate that the success of PIM and the efficacy of image identification software like APHIS is dependent on the quality and standardization of the images collected. The study also underscores the importance of practical experience and continuous learning for the optimal utilization of software like APHIS. Despite occasional false rejected matches, the overall strong performance metrics with low false rejection rate demonstrate that these instances do not significantly impact the reliability of the technique. Thus, this research highlights the importance of careful implementation, continuous learning, and image quality control in leveraging the full potential of image identification software in wildlife studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".