Testing non‐lethal techniques for endoparasite detection and sex determination in pumpkinseed sunfish (<scp><i>Lepomis gibbosus</i></scp>)
Bibliographic record
Abstract
Developing non-lethal techniques to estimate parasite infection is critical for studying disease ecology in wild animals. We tested the effectiveness of coelomic ultrasonographic examination and plasma enzyme markers to detect liver infection with bass tapeworms Proteocephalus ambloplitis (Leidy 1887) as well as the effectiveness of ultrasound in predicting fish sex based on gonad imaging ante mortem in two populations of pumpkinseed sunfish Lepomis gibbosus (L. 1758). We also conducted cytopathological and histopathological analyses on a small subset of fish to investigate the potential for these techniques to detect signs of infection and liver disease. We found that fish sex was correctly identified by ultrasound in 87% of fish screened. There was no statistically significant relationship between parasite density and plasma enzyme concentration in infected fish. However, there were clinical differences between individuals from uninfected and infected populations in the enzymes creatine kinase and alanine transaminase. Histopathology and cytopathology assays confirmed the presence of macrophages and clear signs of inflammation within the liver of infected fish. Our results demonstrate that ultrasound, while useful for sex determination, was not effective in detecting infection in small species like sunfish. However, techniques such as blood analysis and potentially cytopathology are promising tools for parasitic detection in L. gibbosus and warrant further investigation, especially for use in other larger species.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".