Semi-automated identification of individual big brown bats via collagen–elastin patterns in the wing membrane
Bibliographic record
Abstract
Abstract Collagen-elastin (CE) bundle patterns in the wing membrane have been used to identify individual bats; however, this method has not been widely adopted, likely owing to the laborious nature of manually comparing wing images through visual inspection. We tested the effectiveness of using an accessible, feature-based, pattern-recognition software—HotSpotter—to identify individuals using patterns of CE bundles in the bat wing. We collected photos from 24 adult (n = 192 photos) big brown bats (Eptesicus fuscus) and their direct offspring (n = 34 pups; n = 136 photos) by illuminating the ventral surface of the wing with ultraviolet light. Upon running a query match comparison on a selected reference image, HotSpotter ranks every other photo in the database based on an assigned similarity score. We found that HotSpotter correctly presented the top-ranked image as another image of the same individual at higher-than-chance performance. The software also performed better than chance when considering matches to images with the same age (adult/juvenile), sex (male/female), wing side (left/right), and known-relatedness (mother–offspring or twin) to the bat in the queried image. The proportion of correct matches increased with the number of top-ranked images included in the initial query. These results are encouraging because they suggest that pattern-recognition software has the potential to automate recognition of bats based on CE bundle patterns in photos of bat wings. With further refinements in the technology, we think it may be possible to achieve nearly 100% accuracy of individual identification.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".