ESTIMATING THE SIZE OF THE GREATER SNOW GOOSE POPULATION
Bibliographic record
Abstract
Accurate and precise estimation of the size of animal populations is critical to sound management and conservation. The size of the greater snow goose (Chen caerulescens atlantica) population has been monitored since 1965 by means of an aerial photographic survey conducted every spring in southern Quebec, Canada. As the population increased, the estimation of its size evolved from total counts of the birds on photographs (1965–1990) to sampling the photographed flocks (1991–2000). From 1998 to 2000, we implemented a protocol to estimate the proportion of flocks missed by the photographic survey. This was achieved using radiomarked geese that were tracked by independent observers during the aerial survey. The proportion of radiomarked geese detected during the survey was used to estimate the proportion of the population that was photographed. The estimated size of the photographed population was based on a combined stratified ratio estimator using partial counts and visual estimates of flocks in 3 size classes. The estimated size of the photographed population had a coefficient of variation (CV) of 2–6%. This precision was achieved by counting only 15% of the photographed geese on average, which was a large gain in logistical efficiency considering the size of the population. We found no evidence for overdispersion of the number of radiomarked birds (n = 70 in 1998, n = 41 in 2000) encountered in each flock. In 1999, technical problems with radiotransmitters prevented a reliable total population size estimate. In 1998 and 2000, we estimated that the photographic crew missed 11 and 29%, respectively, of the radiomarked geese present. The CV of the total population size estimates were 5.8% in 1998 and 11.1% in 2000. As the proportion of missed flocks increases, the number of radiomarked birds required to obtain a CV of 5% increases with a concomitant increase of cost. We highlight spatial and temporal changes in the spring distribution of greater snow geese staging in southern Quebec and suggest that adjustments of timing and coverage of the surveys will be required to maintain and improve the accuracy of the population size estimates at low cost.
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How this classification was reachedexpand
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 | 0.000 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".