Status of the Shortnose Sturgeon Population in the Savannah River, Georgia
Bibliographic record
Abstract
Abstract The federally endangered Shortnose Sturgeon Acipenser brevirostrum was once abundant in all major coastal river systems from the Saint John River, Canada, to the St. Johns River, Florida. During much of the 20th century, however, populations suffered major declines throughout their range from the combined effects of overfishing, pollution, and habitat loss. Although the species was a charter member of the Endangered Species Act, quantified population assessments are still lacking for many river systems throughout their range. Because river‐specific assessments are critical for evaluating species recovery, the objective of this study was to quantify abundance and annual recruitment of Shortnose Sturgeon occupying the Savannah River, Georgia. Anchored gill nets and trammel nets fished during slack tides were used to sample juvenile and adult Shortnose Sturgeon in their summer holding areas during 2013–2015. Huggins closed‐capture models in RMark were used to derive abundance estimates for each demographic group. The best models estimated that the Savannah River contained 81 (95% CI = 27–264) age‐1 juveniles in 2013, 270 (162–468) in 2014, and 245 (104–691) in 2015. The models also estimated the river to contain 486 (198–1,273) age‐2+ juveniles in 2013, 123 (69–235) in 2014, and 187 (81–526) in 2015. Similarly, the adult population was estimated to be 1,865 (784–4,694) individuals in 2013, 1,564 (1,005–2,513) in 2014, and 940 (535–1,753) in 2015. The results of this study provide the first population estimates available for Shortnose Sturgeon in the Savannah River. Additionally, the results suggest that the Savannah River likely contains the second largest population of Shortnose Sturgeon in Georgia. Future studies are needed in the Savannah River and other South Atlantic river systems to better evaluate Shortnose Sturgeon recovery status and the effects of river‐specific anthropogenic modifications. Received June 27, 2016; accepted September 29, 2016 Published online December 2, 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 | 0.001 |
| 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".