The black brant population is declining based on mark recapture
Bibliographic record
Abstract
ABSTRACT Annual survival and recruitment in black brant (Branta bernicla nigricans) have declined since the 1990s, yet aerial surveys of the global population have been stable or even increasing over the past decade. We used a combination of a Lincoln estimator based on harvest information and band recoveries, and marked‐unmarked ratios in bag checks in 1 harvest area in Mexico to estimate the number of adults in the population during 1992–2015. We produced weighted means from the 2 kinds of estimates for years in which we had data for both, with weights equal to the inverse of the variance of the individual estimates. We treated the black brant population as consisting of 2 subpopulations. One population consisted of breeding black brant on the Yukon‐Kuskokwim Delta (YKD), Alaska, USA, and the other consisted of Arctic (northern Alaska, western Canada, and eastern Russia) breeders, and nonbreeders and failed breeders from the YKD that underwent molt migration to the Arctic. For the global population estimates, we assessed potential bias due to differential marking and harvest of the 2 subpopulations, which was approximately 1%, probably because band recovery rates were similar for the 2 subpopulations. Population estimates declined from 229,980 (average for 1999–2002) to 161,504 (average for 2012–2015). Population estimates based on estimated harvest were variable but more stable in the later years of the study, when larger numbers of brant hunters were included in the sample. We suggest that the combination of Lincoln estimates and bag check data provides a reasonable and cost effective approach to monitoring the population. © 2018 The Wildlife Society.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".