Multi‐image flock size estimation with <scp>CountEm</scp>: A case study with half a million Common Eiders and Greater Snow Geese
Bibliographic record
Abstract
Abstract Many of the methods used for estimating population size from ecological surveys have limitations on precision, cost, and/or applicability. The CountEm method was proposed recently for estimating the number of individuals in large groups from single images. It is simple and efficient, and can be applied to any species. Here we present a case study by applying CountEm to a real ecological survey with 278 images of Greater Snow Geese (Anser caerulescens atlanticus) and Common Eiders (Somateria mollissima) flocks taken from fixed‐wing aircraft in Eastern Canada. First, we evaluated the precision and counting time of CountEm on single images. Second, we developed and tested a new multi‐image version of the CountEm software. We show that flock sizes of N > 35,000 can be estimated on single images in ∼5 min, from counting a sample of ∼200 birds, yielding relative SEs in the 5%−10% range. Processing times increased to 10–20 min when simultaneously processing large numbers of images that contained over half a million birds with only modest increases in relative SE (range: 10%−15%). Our results suggest that CountEm may be used to save time and resources if incorporated into monitoring programs that utilize imagery in the abundance estimates.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".