Can adult Black Sea Bass overwinter in Long Island Sound, USA?
Bibliographic record
Abstract
ABSTRACT Objective We experimentally tested whether adult Black Sea Bass Centropristis striata belonging to the northern stock could theoretically overwinter in Long Island Sound (LIS) and whether doing so would affect their survival, growth, and gonadal investment and the lipid and lean content of their gonad, liver, and white muscle tissues. Methods Fish were caught via hook and line in LIS before and after their offshore winter migration (October 2022 and May 2023, respectively). Fifty individuals from October were reared for 200 d under flow-through conditions and fed diets of crushed mussels or herring. At the end of the experiment, laboratory and wild fish were assessed for their growth, gonadosomatic index, hepatosomatic index, and tissue-specific lipid and lean contents. Results Laboratory fish experienced unfavorable winter temperatures (∼5–12°C) for more than 5 months, exhibiting negligible growth and high mortalities. Mortalities began accruing after temperatures had reached their seasonal minimum of about 5°C in early February (day 120). Mortalities were lower for fish on the mussel diet (40%) than for those on the herring diet (68%), but survivors from the latter group had higher tissue lipid contents. Wild Black Sea Bass returning to LIS in spring had higher tissue lipid contents and greater gonadosomatic indices than surviving laboratory fish on either diet. Conclusions At present, overwintering in LIS appears possible but likely disadvantageous for Black Sea Bass because offshore winter migration results in greater energy reserves and subsequent reproductive investment. In the future, however, warming coastal waters will continue to shorten the duration of unsuitable winter temperatures, which could become conducive to year-round inshore residency or partial migration patterns in the northern stock of Black Sea Bass.
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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 | 0.000 | 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".