The combined effects of temperature and exogenous bacterial sources on mortality in the Eastern oyster (Crassostrea virginica) under anoxia
Bibliographic record
Abstract
Abstract In aquatic environments, low dissolved oxygen concentrations can result in depressed bivalve defense systems while promoting anaerobic bacterial growth, ultimately leading to increased bivalve mortality rates. Although the relationship between low oxygen availability and bivalve mortality has been previously examined, the mechanisms of mortality remain not well understood, limiting our ability to predict mass mortality events. In this study, the effect of anoxia (< 0.1 mgO 2 L −1 ) on adult oyster ( Crassostrea virginica ) mortality rates was explored experimentally using a factorial design, which included the effect of temperature (20°C vs. 28°C) combined with the presence/absence of an exogenous bacterial source (anoxic sediment vs. sterile sediment). Additionally, the effect on oyster mortality rate of removing vs. not removing deceased oysters from the experimental chambers was assessed. Oyster mortality rates, estimated as the time taken for half of the population to die (LT 50 ) in anoxic conditions were significantly affected by temperature, the presence of anoxic sediment, and experimental execution (removing vs. not removing deceased oysters). Temperature had the greatest effect on mortality overall, with high temperatures resulting in increased mortality rates, whereas the presence of anoxic sediment only increased mortality rates consistently at high temperatures. The results of this study suggest that bacterial sources play a role in the mortality rate of oysters under warm anoxic conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".