169 Evaluation and Improvement of the Nutritional Value of co-Products for Swine
Bibliographic record
Abstract
Abstract Feed accounts for the greatest portion of swine production costs, and feed cost has been increasing during the last decades due to increased competition of feed ingredients with the food and biofuel industry. Exposure to climate change is also expected to impact feed cost by changing growing conditions, and recent supply chain disruptions undermine the pricing structure for imported feedstuffs. Therefore, the need for alternative feed ingredients to ensure the economic sustainability of the swine industry cannot be overstated. To this end, utilization of co-products from the agri-processing of cereals and oilseed crops has the potential to help mitigate feed cost and to reduce the environmental footprint of pork production. In general, however, co-products of the agri-processing industry tend to be more variable in their nutrient composition and content of such anti-nutritional factors as non-starch polysaccharides, phytate, and trypsin inhibitors. For these reasons, it is critical that co-products are characterized for the nutritional value and means to enhance their nutritive value examined. We have evaluated a wide range of co-products, including canola meal, camelina meal, hemp meal, extruded soybean meal, wheat co-products, flaxseed meal, etc., and means of enhancing their nutritive values. Examined approaches to ameliorate the negative impact of anti-nutritional factors and optimize energy and nutrient availability in these co-products include: exogenous enzymes supplementation to increase nutrient digestibility, heat treatments to reduce anti-nutritional factors, and particle size reduction to increase nutrient availability. Results of many of our studies clearly demonstrate that the nutritive value of these co-products for swine can be improved significantly through the application of these approaches, thus, offering opportunities to ensure sustainable swine production systems.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".