121 Statistical Analysis Method Counts for Sow Count Data Responses
Bibliographic record
Abstract
Abstract Several statistical analysis methods are typically employed to analyze sow reproductive count data. The research objective was to compare analysis methods of pig birth counts to determine their robustness in identifying simulated treatment differences. Counts of stillborn (SB), born alive (BA) and sow parity differences were simulated using descriptive statistics from a sow farm. Different scenarios were tested: 1) Effect of a 0.5, 1.0, 1.5, and 2.0 percentage point change in treatment difference in SB and BA and, 2) Replicates of 20 to 200 experimental units (EU) in increments of 20 sows; yielding 40 total scenarios. For each scenario, sow observations were simulated 1000 times over. Random sub-setting was used to create a random effect of parity in each dataset as follows: 20% Parity 1, 50% Parity 2–4, and 30% Parity 5+ sows. Each simulated scenario was analyzed as: 1) General linear model (GLM) with raw counts of number of SB or BA as the response variable, 2) GLM with the ratio of BA or SB to total born as the response variable, and 3) Generalized linear mixed model (GLMM) with a binomial distribution of SB or BA as events and total born as trials. Across the EU replicate range, gross performance of models was compared by measuring area under the curve (AUC) with EU as abscissa and the probability of the simulation being P < 0.05 as ordinate. Simulation results are provided in Table 1. The GLMM has elevated probability of detecting true treatment differences over both GLM models for SB and BA. For BA analysis, the GLM Model 1 the probability of detecting true differences is greatly reduced vs. the other two models. This research indicates that deploying GLMM in analyses is a more effective and improved method to detect true differences in sow count data.
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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 | 0.028 | 0.128 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.087 | 0.021 |
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".