An accurate and efficient semiautomated approach to counting birds: Estimating Northern Gannet colony size in Canada
Bibliographic record
Abstract
Abstract Improving the efficiency of population monitoring and conservation programs is beneficial, so long as the accuracy of the information collected is not diminished. The need to expeditiously estimate the population size of seabird colonies is especially acute during mass mortality events when aerial surveys can provide information quickly on the extent of effects and total mortality. In 2022, the highly pathogenic avian influenza virus caused outbreaks at most Northern Gannet Morus bassanus colonies worldwide, killing tens of thousands of gannets in eastern Canada. In this study, we evaluated the accuracy and efficiency of a semiautomated method using the free software CountEm for counting Northern Gannet nests by reanalyzing 13 years of aerial photographs from past population surveys (2009–2020 and 2022). The CountEm program uses a geometric sampling method which overlays a grid of quadrats onto photographs in which the user counts objects of interest. We developed a protocol that generated population estimates that are accurate enough to support population management objectives (i.e., within 2%–5% of manual counts) and outline additional ways to improve CountEm accuracy. Additionally, using CountEm was 1100% more efficient than manually counting based on counting time. Since CountEm relies on human identification of objects to be counted, our methods, results, and conclusions are transferable to any taxa that form large aggregations and can be identified and counted in photographs.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".