Comparing abundance estimates from closed population mark-recapture models of endangered adult Atlantic sturgeon
Bibliographic record
Abstract
Atlantic sturgeon Acipenser oxyrinchus oxyrinchus were listed as 5 distinct population segments under the US Endangered Species Act in 2012.At that time, only 2 abundance estimates of the Atlantic sturgeon population were available: one from commercial fisheries landings in the Hudson River ending in 1995 and one from mark-recapture research in the Altamaha River, Georgia, in 2004 and 2005.In 2013, we verified spawning in the York River, Virginia, system and initiated a multiple-year mark-recapture study focusing on spawning-run abundance.We used a Schumacher-Eschmeyer model and Program CAPTURE to produce estimates of annual spawning abundances from 2013 to 2018.The Schumacher-Eschmeyer estimates of spawning-run abundance with 95% confidence intervals from 2013 to 2018 were 75 , 157 (115-244), 184 (150-238), 222 (137-576), 212 (157-328), and 145 (89-381), respectively.Because Atlantic sturgeon do not spawn every year, the trends in estimates do not suggest a recovering or declining population, but rather variability in proportions of the adult population that return to spawn each year.The estimates produced in Program CAPTURE using M 0 (null), M t (Chao M t and Darroch), M h (Chao M h and Jackknife), and M th (Chao M th ) models all produced similarly reliable estimates.The models that consider a behavioral response to initial capture (M b , M bh , and M tb ) failed to produce reliable estimates for these data, likely because as an endangered species, the dataset for Atlantic sturgeon was sparse.The Jackknife equation (model M h ) was the most precise every year with reliable accuracy and therefore is recommended.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".