Inferring bird communities on remote freshwater lakes through time-lapse imagery
Bibliographic record
Abstract
Assessing bird diversity and associated ecological patterns in remote freshwater lakes presents challenges that require innovative approaches. Here, we evaluated the utility of time-lapse images from camera traps for this purpose using two lakes in Haida Gwaii, British Columbia, Canada. We consider four key factors: (1) manual versus automated image processing, (2) data validation through in-person observations, (3) the ability of time-lapse data to capture known ecological patterns, and (4) variation in sampling effort. We find that (1) MegaDetector, a common AI approach, is not effective at detecting birds from time-lapse images—necessitating manual screening, (2) relative bird abundances were correlated between time-lapse and in-person observer data, (3) time-lapse data capture previously documented ecological variation in space and time, and (4) sampling effort per camera trap can be, under certain scenarios, scaled down, but camera trap position and time-lapse frequency greatly influence bird detectability. Our research builds on the few previous studies that use time-lapse imagery to detect birds, and our work is the first to focus on detecting ecological patterns on freshwater lakes in remote landscapes. Camera trap technologies can shed light on avifauna in remote freshwater lakes, but additional developments are needed to maximize utility of such applications.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".