Estimating spawning green sturgeon (<i>Acipenser medirostris</i> Ayres, 1854) abundance in the Sacramento River using side-scan sonar and N-mixture models
Bibliographic record
Abstract
Current estimates of the threatened southern distinct population segment of the North American green sturgeon ( Acipenser medirostris) combine a plot-sampling density estimator with Dual frequency IDentification SONar (DIDSON) and adaptive resolution imaging sonar (ARIS) sonar data. From 2020 to 2022, we annually collected images of all known green sturgeon aggregations and compared the established method to an N-mixture model using side-scan sonar images. We compared 18 different N-mixture model combinations and chose an overdispersed Poisson model that produced estimated abundances of 742, 1286, and 1208 for 2020–2022, respectively. These numbers are ∼2 times greater than the previous method and, if sustained, would fulfill a key criterion for green sturgeon recovery. N-mixture models are known to be sensitive to violations of assumptions, such as the highly dispersed data from our study that caused serious issues, and we recommend practitioners make judicious use of overdispersion and goodness-of-fit tests and be able to identify parameter confounding between detectability and abundance estimates. For our green sturgeon, we recommend simpler population estimates and to focus future energy on reducing variability in the data collection process.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".