Use of drones for the creation and development of a photographic identification catalogue for an endangered whale population
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 it