Use of drones for the creation and development of a photographic identification catalogue for an endangered whale population
Bibliographic record
Abstract
Photographic identification is increasingly being used as a cost-effective and minimally invasive method to monitor species, which is of particular importance for endangered populations that are vulnerable to intrusive research methods. The purpose of our study was to collect photographs of an endangered population of beluga whales ( Delphinapterus leucas (Pallas, 1776)) in Cumberland Sound, Nunavut, Canada, for use in photographic identification. Rather than pursuing the whales with boats to collect photographs, drones were used to minimize disturbance. We analyzed drone photographs from 2017 to 2019 for distinctive markings on the whales, which were used to develop a photographic identification catalogue. In total, 93 individuals were identified, with 24 resightings of marked individuals over the survey period. Approximately 43.4% (standard error 3.3%) of the adult beluga population was uniquely marked. The beluga population has been harvested at a rate of 41 whales per year, not including struck and lost, since 2002. The markings were from unknown origins (61%), scars/wounds from gunshots (27%), anthropogenic or predatory given the size and severity (11%), or a satellite tag (1%). The continuation of the photographic identification program will allow for the estimation of important population demographics, such as abundance and calving interval, which are important parameters for population conservation and management.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".