Recruitment of Juvenile Atlantic Sturgeon in the Savannah River, Georgia
Bibliographic record
Abstract
Abstract Atlantic Sturgeon Acipenser oxyrinchus oxyrinchus were once abundant along the Atlantic coast of North America from the Saint Lawrence River, Canada, to the St. Johns River, Florida. Severe overfishing, coupled with habitat losses during the 1900s, resulted in major population declines that eventually led to the subspecies’ listing under the U.S. Endangered Species Act in 2012. Despite this listing, quantified recruitment data are largely lacking for most Atlantic Sturgeon populations, particularly those within the South Atlantic distinct population segment. The objective of this study was to quantify annual recruitment of Atlantic Sturgeon in the Savannah River, Georgia, by estimating annual abundance of age‐1, river‐resident juveniles. During the summers of 2013–2015, we used anchored gill nets and trammel nets to sample juvenile Atlantic Sturgeon throughout the Savannah River estuary. Ages of captured juveniles were determined by using length‐frequency analysis, and abundance of each juvenile age‐class was estimated with Huggins closed‐capture models in RMark. We estimated the Savannah River to contain 528 age‐1 juveniles in 2013, 589 in 2014, and 597 in 2015. The results from this study indicate that the Savannah River population is likely the second largest within the South Atlantic distinct population segment. Future studies are needed to determine the relative importance of the Savannah River as a natural source of recruitment for smaller, more imperiled populations in adjacent rivers. Consequently, we suggest that management efforts continue to prioritize the protection of both the population and the associated critical habitats within the Savannah River estuary. Received April 7, 2016; accepted June 28, 2016 Published online September 23, 2016
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".