Bat selfies: photographic surveys of flying bats
Bibliographic record
Abstract
The recent pandemic and other environmental concerns have resulted in restrictions on research and surveys involving capture and handling bats. While acoustic surveys have been widely used as an alternative survey method, in this study, we show how photographic surveys can offer an important contribution to study and survey bats. We outline approaches, using high speed flash and automated trip beams to obtain photos of flying bats of sufficient quality for reliable identification of species. We show, through a series of examples of setups and photographs, that photography is effective for surveying bats at a variety of sites, where bats roost, drink, and forage. We note, however, that photographic surveys cannot replace capture in all situations. In addition, although photographing bats is less invasive than capturing them, it can involve disturbance, so we stress the importance of minimizing the impact of such operations on bats. Supplementary Information: The online version contains supplementary material available at 10.1007/s42991-022-00233-7.
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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 | 0.001 | 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 | 0.001 | 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 | 0.000 |
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".