Assessing total mortality following seabird wrecks given variable data quantity and quality: the Cassin’s auklet die-off
Bibliographic record
Abstract
Mass mortality events (MMEs) of seabirds are becoming more frequent as the global climate warms. Often documented via beached bird surveys, methods for estimating event-wide mortality are needed that can accommodate regional differences in carcass deposition and data quality/quantity. We develop a framework for estimating mortality from beached bird counts, extending existing approaches through the novel application of ocean circulation modeling to assess beaching likelihood. We applied our framework to the 2014/15 Cassin’s auklet ( Ptychoramphus aleuticus) MME, which spread across three regions (central California, northern California-through-Washington, British Columbia) with varying data quality/quantity. Our best mortality estimate of ∼400 000 (estimates ranged from 265 000 to 700 000 depending on model uncertainty and extent) places this seabird MME as one of the largest on record. However, we caution that much uncertainty exists surrounding model parameterization and deposition in British Columbia where beached bird data were sparse. We suggest that the application of ocean circulation models, combined with process-based modeling of carcass persistence and detection, can improve estimates of MME magnitude.
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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 | 0.004 | 0.017 |
| 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 | 0.001 |
| 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".