Data-driven modeling of reproductive performance: a cohort study for elevated sow efficiency and sustainability in livestock farming
Bibliographic record
Abstract
The slow development of the Internet of Things (IoT) in pig production, due to the lack of high-quality data, limited large-scale models, and low hardware coverage, has hindered the widespread adoption of precision feeding practices. This study aimed to address these challenges by providing a standardized dataset as a foundation for IoT development and constructing predictive models focused on birth litter weight (BLW) and weaned litter weight (WLW). To achieve these objectives, two comprehensive datasets consisting of 10,089 sow characteristics were collected. By comparing eight different algorithms, GBDT algorithm was selected as the optimal algorithm for modeling of BLW and WLW. The datasets were divided into a 90 % sample for model derivation, with the remaining 10 % used for model validation. The models for both BLW and WLW datasets exhibited consistent performance between main and validation cohorts, with low error magnitudes and high relative accuracy (MAE: 1.8–2.5, MAPE: 2.55 %–18.41 %, R > 60 %), indicating robustness and generalizability to unseen data. Delving deeper, the SHAP summary plots illustrated that in the model for BLW, G.ADFIp2, G.ADFIp3, G.ADFIp4, G.ADF and parity had a significant impact on the prediction. In the WLW model, the key influencing factors were weaned litter size, duration of lactation, parity, and birth litter weight. SHAP force and dependence plots had uncovered intricate effects of various features on the model’s outcomes. To enhance accessibility, we developed a user-friendly visualization and prediction website using the Streamlit Python framework. These critical research findings provide decision-makers with invaluable insights, fostering advancements in precision feeding models and IoT technologies in the swine industry. Ultimately, this contributes to the overarching goal of enhancing the comprehensive sustainability of livestock farming.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".