24 Field experience and challenges facing sow production today, industry strategies
Bibliographic record
Abstract
Abstract I started in the swine industry in 1973 and served for the last 16 years as one of the owners of Swine Management Services (SMS), LLC. I have spent time in a lot of swine facilities of all sizes and ages, and I have seen lots of ideas tried and changes made both positive and negative. I feel that good sow data is your road map to monitoring farms and changes as they are made. SMS has created a company that takes sow reports, does the analysis, and sends written reports to the farm and management for review. SMS currently works with over 450,000 sows in the industry. The farm benchmarking program has 1.6+ million sows from 900+ farms in the United States, Canada, and Australia with data goes back 13 years. It compares farms based on pigs weaned / mated female / year with range of <18 to 34+ pigs. Top farms have figured out the need for quality labor, and they know that gilts are the key to the future—and they will make farrowing changes to improve day 1 care procedures to save more of those pigs. We now see farms with total born at 16+ pigs, pigs weaned per litter at 13+ pigs, pigs weighing 13+ pounds at 19 day weaning age, and sows after weaning coming back into heat in less than 5 days with 95+% breed by day 7. What are their bodies going through? I feel that the ability to manage and feed these high-producing females needs researching. Will that include a lot of work on the nutrition side, floors for sows in lose sows housing, and free stalls in lactation? Where is the trained labor needed coming from?
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 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 | 0.019 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".