Integrating side-scan sonar and acoustic telemetry to estimate the annual spawning run size of Atlantic sturgeon in the Hudson River
Bibliographic record
Abstract
There is considerable interest in evaluating the status and trends of sturgeon populations, yet many traditional approaches to estimating the abundance of fishes are intractable due to their biology and rarity. Side-scan sonar has recently emerged as an effective tool for censusing sturgeon in rivers, yet challenges remain for censusing open populations that may visit specific habitats over periods of time (e.g., spawning runs). We use a hierarchical model to integrate side-scan sonar with acoustic telemetry, to estimate the proportion of a spawning run fitted with acoustic tags (12%; 95% CrI = 8%–16%) and extrapolate to the total run size in 2014. Our investigation represents a novel approach to generating run size estimates in a large river and provides the first estimate of Atlantic sturgeon (Acipenser oxyrinchus oxyrinchus) spawning run size for the Hudson River ([Formula: see text] = 466; 95% CrI = 310–745) since the fishery moratorium in the 1990s. Our estimate suggests that the Hudson River holds one of the largest contemporary populations of Atlantic sturgeon, but also indicates that it remains sharply depleted relative to virgin conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".