Distribution of nutrients across the edible components of a modelled typical Australian lamb: A case study
Bibliographic record
Abstract
Abstract Achieving sustainable development is one of the greatest challenges for humanity. This includes producing food in a way that enhances ecosystem, animal and human health, at the farm level and more broadly. To measure the enhancement brought about by animal production systems, producers, livestock industries and governments need a deeper understanding of the nutrient distribution across the edible parts of the animal. This case study examined the nutrient distribution across food products (carcase and co-products (edible offal and slaughter fat)) derived from a typical Australian lamb, using modelling with secondary data. Due to data gaps, some edible offal products were not able to be incorporated into the model (blood, trachea, omasum, abomasum, intestines, feet/tendons and head meat). Co-products accounted for approximately 24% of total edible product (i.e., carcase and co-product) by weight, 18% of the total protein and 37% of the total fat. With regards to micronutrients, the co-products contained 42% of the total iron content and the liver had more vitamin A, folate and vitamin B 12 than the carcase and other co-products combined. This case study highlighted the nutritional value of co-products, especially liver, in the context of the whole animal and, the importance of including co-products in assessments of animal production systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".