Concave relationship between growth of masu salmon <i>Oncorhynchus masou</i> and abundance of the parasite <i>Salmonema</i> cf. <i>ephemeridarum</i> (Nematoda, Cystidicolidae)
Bibliographic record
Abstract
Under the common definition, parasites generally exploit and have negative effects on hosts. Intestinal parasites, including species of nematodes, are believed to be less harmful, but the impact on the host generally depends on the degree of parasitic infection. We explored the relationship between the number of Salmonema cf. ephemeridarum (Linstow, 1872), a nematode parasite, infecting masu salmon Oncorhynchus masou (Brevoort, 1856) and growth of the salmon in an outdoor enclosure system . We found a concave relationship between the growth of masu salmon and the number of S. cf. ephemeridarum. This suggests that the negative effect of this parasite on host growth becomes apparent only when the parasite abundance exceeds a certain value. Although the mechanism behind this phenomenon remains unclear, this study demonstrated an uncommon relationship between host growth and parasite abundance. Our results imply that the effect of parasitism on host growth can reverse depending on parasite number. This concave function model can be a foundation for further comprehensive analysis.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".