Distribution, causes and significance of the Summer Mortality syndrome in the Pacific oyster (Crassostrea gigas) and in other bivalve species
Bibliographic record
Abstract
Several bivalve species (e.g. oysters, blue mussels, scallops) are affected by Summer Mortality in different countries. However, most research programmes are focused on the Pacific oyster (Crassostrea gigas) because of its worldwide commercial importance. The first description of the Summer Mortality syndrome concerned the Pacific oyster in Japan in the 1940s. The syndrome was, and continues to be, associated with high mortality of Pacific oysters and other bivalves around the world (Japan, USA, Canada, China and France). The causes remain unclear, but a multifactorial aetiology is suspected. The collective evidence suggests that Summer Mortality involves a suite of intrinsic and extrinsic factors. The most important extrinsic factor seems to be elevated temperature coming at a time when the intrinsic factors, gametogenesis and spawning, place the animal in a relatively unstable physiological condition. Any other external factor that exacerbates this instability, including e.g. high food availability, physical stressors or pathogens, may push the animals over a threshold from which they cannot recover.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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".