An integrated population model and sensitivity assessment for a data-poor population of green sturgeon
Bibliographic record
Abstract
Conservation planning requires understanding population sizes and trajectories. As with many imperiled species, green sturgeon ( Acipenser medirostris) census data are limited, making it difficult to contextualize current threats and promote quantitative conservation goals. We combined spawning census, growth, and demographic data for the southern population of green sturgeon to build a population size and trajectory estimate. We generated a distribution of population size estimates and trajectories reflecting uncertainty from multiple sources. We then propagate these estimates through a demographic model to assess the potential impact of fishing bycatch. Our model suggests the population is below the recovery goal of 3000 adults. The most probable current total population estimate (including juveniles) is approximately 10 000 fish (5300–18 400 95% high density interval (HDI)), with 2400 adults (2197–2624 95% HDI). Simulated fishing bycatch pressure on the adults and subadults reduced abundance by a median value of 0.4% per year, which could be an impediment to recovery. Fisheries bycatch is one of many threats this population faces; this integrated framework may be used to assess how other threats may affect this population.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".