A comparative analysis of machine learning classifiers for modeling the number of liveborn piglets
Bibliographic record
Abstract
The profitability of pig farms is largely dependent on the productivity of their sow herds, often measured as the number of piglets weaned per sow per year, which is closely linked to the number of liveborn piglets (NLB) per sow per litter. To improve farming efficiency, underproducing sows are often culled and replaced to reduce and compensate for their maintenance costs. However, wrongly culling highly productive sows incurs costs associated with replacement and missed opportunities. Therefore, the ability to distinguish between sows with high and low productivity is very valuable. This study evaluated the predictive performance of six traditional and three ensemble machine learning classification models to predict whether NLB in the subsequent parity is "Low" (NLB < 13), "Medium" (13 ≤ NLB ≤ 16) or "High" (NLB > 16). This evaluation was conducted using data collected during the current parity from two distinct farm settings: the CDPQ Dataset (415 sows, 468 parity records, 1 research farm) from a research farm, and the Hypor Dataset (11,633 sows, 27,547 records, 12 commercial farms). Six input production measurements were common across both datasets: parity, gestation length, lactation length, current parity NLB, and the number of stillborn, mummified and weaned piglets. The CDPQ Dataset included 6 additional input production measurements: body weight (BW) and backfat thickness (BFT) measured at the time of breeding, farrowing and weaning. Classifiers used these input variables to generate predictions, and their performances were assessed using weighted F1-Score. The best-performing classifier for the CDPQ and the Hypor Datasets was Stochastic Gradient Descent (SGD), which achieved the highest weighted F1-Score of 0.37 and 0.45, respectively. In addition, by removing the six BW and BFT variables from the CDPQ Dataset (reduced CDPQ Dataset), the SGD classifier only attained a weighted F1-Score of 0.43, likely caused by information redundancy and limited dataset size. Despite improved performance, variable importance analysis for the CDPQ dataset identified BW and BFT at weaning as the key predictors, suggesting that including these traits could similarly enhance models trained on the Hypor dataset. Overall, the study demonstrated that machine learning classifiers show promise for forecasting sow productivity, though further research with more extensive and higher-quality datasets is required before broad industry adoption.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".