20 How do Producers and Nutritionists Determine Which Levers to Pull to Optimize Feed Cost and Profitability Under Differing Economic Conditions?
Bibliographic record
Abstract
Abstract Numerous factors including geopolitical unrest, supply chain disruption, government policies, and hyperinflation have resulted in a global increase in the cost of feed ingredients. Pork producers and swine nutritionists continuously answer the tricky question of how to best manage feed costs to maximize profitability under an everchanging economic landscape. Applied nutritionists must design and implement feeding programs that take into account and balance several key nutrition levers including energy, SID lysine to energy ratio, SID amino acid to lysine ratios, and feed processing along with factors including ingredient prices, pork price, barn space, feed mill throughput, and pig livability. Biological and economical models are developed and utilized to assist with prediction of key performance and profitability outcomes leveraging results from published and unpublished controlled research trials along with historical production data. The objective of this presentation is to provide insights into the decision-making process for feeding program design and implementation under current and theoretical economic situations.
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 | 0.022 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".