Factors influencing disease-induced mortality of Pacific oysters Crassostrea gigas
Bibliographic record
Abstract
OsHV-1 Var have been observed in many oyster-producing countries since 2008. The present study, comprised of 4 complementary experiments, aimed to identify factors associated with disease-induced oyster mortality in order to propose mitigation strategies. Our first experiment compared survival of oysters from natural spatfall with others sampled from nurseries, after thermal elevation in the laboratory from <14 to 21C. A total of 60% of the tested wild seed batches (n = 51) were infected by OsHV-1, exhibited mortality and were able to transmit the disease to cohabited nave oysters. Comparatively, only 1 out of the 32 tested batches sampled from nurseries presented similar characteristics. In a second experiment, we studied the effects that timing and duration of exposure to field conditions had on risk of infection and mortality in the laboratory at 21C. Nave oysters deployed in the field during winter and spring, when seawater temperatures were <14.7C, showed no mortality in the laboratory, and OsHV-1 DNA was not detected by PCR. However, in oysters transferred to the field, OsHV-1 was observed when seawater temperature reached ~15.3C. Our third experiment showed that the odds of mortality decreased with age of oysters when facing the disease. Further, we observed that odds of disease mortality decreased with water renewal and increased with the biomass of neighbouring infected oysters under controlled conditions. Based on these findings, we propose mitigation strategies in terms of the regulation of oyster movements between sites, timing of seeding and spatial planning, taking into account seawater temperature and seed origin.
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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".