Abundance of Bering-Chukchi-Beaufort bowhead whales (Balaena mysticetus) in 2004 estimated from photo-identification data
Bibliographic record
Abstract
Ice-based surveys near Point Barrow, Alaska, have been used to obtain most estimates of abundance for the Bering-Chukchi-Beaufort (B-C-B) stock of bowhead whales, but global warming has raised concerns that ice-based surveys may not be practical in the future. Aerial photographic surveys provide an alternative method for obtaining abundance estimates and may replace ice-based surveys. Aerial photographic surveys were conducted near Point Barrow during the spring migrations of bowhead whales in 2003 and 2004 and, in 2005, in the northern Bering Sea in spring and near Barrow in fall. The 2003 survey was the most complete photographic survey of the population conducted to date. These surveys provided photo-identification data for use in capture-recapture analyses. A screening procedure was used to define which whales captured in 2003, 2004 and/or 2005 were marked and could be reidentified if photographed on another occasion. An estimate of the number of marked whales was obtained using a closed population model for capture-recapture data. Several models were investigated, including models that accounted for heterogeneity in capture probabilities, but a simple model with no covariates produced the most precise estimate. To account for unmarked whales, the estimate of marked whales was divided by an estimate of the proportion of the bowhead population that was marked based on the 1989–2004 spring photographic surveys near Point Barrow. Abundance of the B-C-B bowhead population in 2004 (excluding calves) was estimated to be 12,631 with CV 0.2442, 95% bootstrap percentile confidence interval (7,900; 19,700) and 5% lower limit 8,400. These results were compared with results that used approximate variance expressions for the estimates of the number of marked whales, the proportion of the population that was marked and population abundance instead of using the bootstrap. The estimates of abundance in 2004 computed for comparison included one based on a modified Petersen estimate of the number of marked whales that omitted the 2005 data as well as the estimate of 12,631 described above. The comparison estimates also included estimates of abundance in 1985 computed from 1984–87 photographic survey data using the same methods. All the abundance estimates computed from photographic data were consistent with expectations based on independent abundance and trend estimates from the ice-based surveys conducted from 1978 to 2001.
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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 | 0.001 |
| 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".