The combined effects of temperature and exogenous bacterial sources on mortality in Crassostrea virginica under severe hypoxia
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 rate. There are discrepancies between laboratory and field studies examining bivalve mortality under low oxygen conditions, possibly leading to an underestimation of the impact of hypoxic events. Indeed, laboratory studies typically exclude potentially influential factors that may affect survival, e.g., exogenous bacteria. In this study, adult oyster (Crassostrea virginica, 60 ± 5 mm shell length) survivability was investigated during severe hypoxia (< 0.1 mgO2L− 1) in combination with high temperature (20˚C vs. 28˚C), and the introduction of a secondary bacteria source (anoxic marine sediment). In addition, an experiment tested if the conventional methodological approach in these types of experiments, i.e., removing dead bivalves from the population, impacted survivability. Results demonstrate that at the highest tested temperature (28˚C) the effect of a secondary bacterial source did not significantly impact survival rates (time taken for half the population to die (LT50) (LT50: 9.7 ± 0.5 vs. 10.9 ± 0.4 days secondary bacterial source vs. no secondary bacterial source, respectively). However, at the lower temperature (20˚C) the presence of a secondary bacterial source did decrease survival rates (LT50: 9.8 ± 0.4 vs. 13.7 days bacterial source vs. no secondary bacterial source, respectively). Additionally, dead oyster removal increased oyster survivability in all treatments relative to when they were not removed. This study highlights the mechanisms by which mortality rates are underestimated in laboratory compared to field studies.
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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".