117 Nutrition Modelling: What Can the Pet Field Learn (or Steal) from Recent Directions in Other Species?
Bibliographic record
Abstract
Abstract Nutrition modelling has been the cornerstone of feed formulation and diet optimization in animal production systems for decades. Since the 1970s and 1980s, mechanistic models of nutrient digestion, absorption, metabolism, growth and milk/egg production have been developed and implemented to (1) amass our cumulative biological knowledge and develop theories of regulation, (2) identify knowledge gaps, and (3) propose means to manipulate nutrient dynamics in the animal. At the nutrient and metabolite level, many commonalities exist and parallels found between species. In fact, several second generation models originate from other species or research fields, and many current/existing models may be advanced by examination and consideration of models developed in other species. Many such mathematical models are implemented in practice as ‘decision support systems’ or ‘opportunity analysis tools’, in order to examine a variety of (feeding or management) scenarios for their potential outcomes, with the goal of providing targeted nutrition, improving performance, reducing cost and minimizing environmental impact. More recently, partnering artificial intelligence/machine learning modelling methodologies with newly available big data streams has ushered in a new era of possibilities for data extraction and modelling in animal systems. The niche for this type of modelling in animal production appears to be (1) pattern recognition (e.g. disease detection, activity) and (2) strong predictive/forecasting abilities (e.g. bodyweight, milk, egg production). There also appears strong potential for these two seemingly divergent modelling approaches to be integrated – for example, in precision feeding systems, or in utilizing the abundance of sensor data to better drive or develop causal-pathway based mechanistic models. This talk will broadly review trends and advances in agriculture animal species modelling, and suggest what may be borrowed, stolen or serve as inspiration to advance nutrition models in companion species.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".