PSIII-8 Difference between two fecal egg count methods and estimation of genetic parameters for gastrointestinal parasite resistance traits in sheep
Bibliographic record
Abstract
Abstract Fecal egg count (FEC) is an indicative measurement for parasite infection in sheep. Different FEC methods may show inconsistent results. Not accounting for inconsistencies can be problematic when integrating measurements from different FEC methods for genetic evaluation. The objectives of this study were to: evaluate the difference between two FEC methods, the Modified McMaster (LMMR) and the Triple Chamber McMaster (LTCM); estimate the genetic and phenotypic correlations between records from two methods; and estimate genetic parameters for FEC and other gastrointestinal parasite resistance traits. A total of 1,676 fecal samples were collected from a commercial sheep farm between 2012 and 2019. Fecal egg counting was performed using the Modified McMaster (n = 998) and the Triple Chamber McMaster (n = 678) methods. Other parasite resistance trait records were collected from the same farm including eye score (FAMACHA©), body condition score (BCS), and body weight (WT). The mean and variance between the two FEC methods were significantly different (P < 0.0001), but phenotypic and genetic correlations between them were high (0.88 and 0.94, respectively). Therefore, pre-adjustment is required prior to integrating data from the different methods. For multiple trait analysis with other parasite resistance traits, data from the two fecal egg counting methods were integrated (LFEC) by using records for the LMMR when available and replacing missing records with standardized LTCM records for mean and variance of LMMR. Heritability estimates were 0.12, 0.07, 0.17, and 0.24, for LFEC, FAMACHA©, BCS, and the WT, respectively. Estimated genetic correlations between fecal egg count and the other parasite resistance traits were low with FAMACHA© (0.24), BCS (-0.03), and WT (0.22), suggesting little to no benefit of using such traits as indicators for LFEC.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".