Impact of elevated temperature on the growth, survival, and trophic dynamics of winter flounder larvae: a mesocosm study
Bibliographic record
Abstract
Winter flounder (Pseudopleuronectes americanus) is a dominant commercial fish in Narragansett Bay, Rhode Island, and yet factors controlling its recruitment remain unclear. An experiment was conducted with six 13-m3 land-based mesocosms (5 m deep) from February to April 1997 to address the impact of increased temperature (+3°C) on growth, survival, and trophic dynamics of winter flounder larvae. Objectives were to determine if warmer winter temperatures result in lower survival of winter flounder as a result of increased predator activity or if temperature-induced alterations in the food web result in greater food availability, perhaps leading to increased survival. Analyses of variance revealed significant (P < 0.05) or near-significant (P < 0.10) differences in phytoplankton and zooplankton abundance and biomass between warm and cool mesocosms. Winter flounder egg survival, percent hatch, time to hatch, and initial size were significantly greater in cool systems (P < 0.05). Mortality rates were lower in cool systems and significantly related to the abundance of active predators (P < 0.05). The cumulative impact of decreased survival of eggs and larvae in warm systems may partially explain the decline of winter flounder in Narragansett Bay, which has experienced elevated winter water temperatures in recent years.
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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 | 0.000 | 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".