Accurate abundance estimation of cliff-breeding Bounty Island shags using drone-based 2D and 3D photogrammetry
Bibliographic record
Abstract
Effective seabird management strategies rely on accurate population estimates, with previous methods typically employing ground counts of a target species. However, difficult and often inaccessible breeding habitats are now able to be explored due to recent technological advancements in Unoccupied Aerial Vehicles (UAVs). This study tested a novel approach by combining high-resolution orthomosaics and 3D models to provide population estimates of the remote cliff-breeding Bounty Island shag (Leucocarbo ranfurlyi) on the sub-Antarctic Bounty Islands in November 2022. Our results report 573 breeding pairs, estimating a total population of approximately 1733 birds, breeding on 13 of the 14 main islands. Given the topographical constraints of surveying the islands by boat, the most comparable assessment in 1978 shows a similar count of breeding pairs, proposing the Bounty Island shag population is stable. However, long-term monitoring and additional research surrounding foraging strategies is crucial for developing conservation efforts for one of the rarest and spatially restricted shag species in the world. Our study demonstrates a reproducible method for estimating elusive wildlife populations that can be used across species with wider 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".