Plastron color patterns allow for individual photo-identification in two different chelonian species
Bibliographic record
Abstract
Natural marks have increasingly been used as a tool for individual identification in capture–mark–recapture techniques. Photo-identification is a noninvasive alternative to traditional marking techniques, allowing individual recognition of species through time and space. We tested the APHIS (Automatic Photo Identification Suite) software as a software capable of identifying individuals of Hermann’s Tortoise ( Testudo hermanni Gmelin, 1789) and European Pond Turtle ( Emys orbicularis (Linneaus, 1758)) in different populations during capture–release sessions in the field based on plastron color patterns, since they can be used as natural marks for identification. For this individual identification, spot pattern matching (SPM) and image template matching (ITM) procedures were tested, achieving 100% success of individuals recognized in both procedures and visually verified by comparing the images. However, the ITM procedure was more efficient at recognizing recaptures than SPM because ITM allowed faster recapture verification, since most of the matches were directly placed in the first position on the candidate list. Previous studies used photo-identification on freshwater or sea turtles but never with terrestrial tortoise species. Consequently, it was corroborated that APHIS is a competent and efficient software considering photo-identification of T. hermanni and E. orbicularis, and that it can be applied to close species with similar and unique individual color patterns in their plastron.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".