Augmentation of Two Identification Methods for Bats
Bibliographic record
Abstract
All marking methods for identifying bats (order Chiropteran) have practical limitations, with no one method being superior to others. To address these limitations, we proposed and tested the use of two prospective identification methods p-Chip microtransponder tags and the use of collagen-elastin (CE) bundle patterns as a biomarker in a captive colony of big brown bats (Eptesicus fuscus). For p-Chips, we assessed (1) animal handling time, (2) scan time, (3) number of wand flashes, (4) p-Chip visibility, (5) readability, and (6) the bat’s overall condition for two locations: all bats had p-Chips implanted in the wing (n = 30) and some of these bats also had p-Chips implanted in their leg (n = 13). For both locations, average scan times increased over time whereas the number of wand flashes decreased, suggesting p-Chip recording efficacy improves with user experience. The visibility and readability of p-Chips was consistently better for tags injected in the wing compared the leg, emphasizing the wing as the preferred implantation site. A second proposed identification method extends upon the use of manual, visual inspection (Amelon et al. 2017) to examine whether pattern-recognition software can accurately detect and identify individual bats using the pattern of collagen and elastin bundles in the wing. We tested the effectiveness of HotSpotter© to identify adult (n = 24 bats; n = 192 photos) and juvenile (n = 34 pups; n = 136 photos) E. fuscus by comparing photos of the wing membrane illuminated by ultraviolet light. We then assessed similarity scores between adults and juveniles separately and quantified the occurrence of correct and incorrect matches. For images of adult bats, 60% of comparisons resulted in a correctly matched top-ranked image (i.e. an image of the same bat was most similar), whereas 27% of comparisons had a correct top-ranked image for wing membrane photos of juvenile bats. The success rate of obtaining a correct match could be increased by including a larger subset of top-ranked images when selecting possible correct matches. Altogether, these results suggest that p-Chip tags and potentially the use of HotSpotter pattern recognition software are suitable methods for identifying captive E. fuscus and may be viable for use in the field and in other bat species.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".