The relationship between flesh quality and numbers of <i>Kudoa thyrsites</i> plasmodia and spores in farmed Atlantic salmon, <i>Salmo salar</i> L.
Bibliographic record
Abstract
Atlantic salmon, Salmo salar L., were exposed to Kudoa thyrsites (Myxozoa, Myxosporea)-containing sea water for 15 months, and then harvested and assessed for parasite burden and fillet quality. At harvest, parasites were enumerated in muscle samples from a variety of somatic and opercular sites, and mean counts were determined for each fish. After 6 days storage at 4 degrees C, fillet quality was determined by visual assessment and by analysis of muscle firmness using a texture analyzer. Fillet quality could best be predicted by determining mean parasite numbers and spore counts in all eight tissue samples (somatic and opercular) or in four fillet samples, as the counts from opercular samples alone showed greater variability and thus decreased reliability. The variability in both plasmodia and spore numbers between tissue samples taken from an individual fish indicated that the parasites were not uniformly distributed in the somatic musculature. Therefore, to best predict the probable level of fillet degradation caused by K. thyrsites infections, multiple samples must be taken from each fish. If this is performed, a mean plasmodia count of 0.3 mm(-2) or a mean spore count of 4.0 x 10(5) g(-1) of tissue are the levels where the probability of severe myoliquefaction becomes a significant risk.
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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".