Occurrence of Ovary Enlargement Disease of Pacific Oyster by Introduction of Infected Spat in Eastern Hokkaido, Japan
Bibliographic record
Abstract
Pacific oysters Crassostrea gigas infected with the ovarian protozoan parasite Marteilioides chungmuensis often develop ovary enlargement disease characterized by nodular-like ovaries, resulting in a loss of marketability due to their unattractive appearance. This disease was previously limited to oyster farms in western Japan, but recently a new occurrence was recognized in an open water oyster farm in eastern Hokkaido, the northernmost region of Japan. In this study, we developed a quantitative real-time PCR to detect the causative parasite and conducted an epidemiological survey in the oyster farm from April 2021 to March 2022. Prior to introduction to the farm, parasite DNA was detected in oysters originating from Miyagi Prefecture (Miyagi oysters), then infection of the parasite was histologically confirmed, suggesting that Miyagi oysters were already infected with the parasite before introduction to the oyster farm in Hokkaido, leading to the development of the disease. Conversely, in oysters produced in a local hatchery in Hokkaido (local oysters), parasite DNA was not detected prior to introduction to the farm, but detected in August and September after introduction. Although infection was not confirmed histologically in local oysters during the survey period, transmission of the parasite in the survey area needs to be further examined.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".