Bibliographic record
Abstract
Abstract Livestock farming systems (LFS) have detrimental effects on the environment, associated mainly with feed production, enteric methane and emissions from manure. LFS are complex because they are structured by multiple interactions between biological and human-controlled processes, at various organizational levels. Their behavior and the extent to which feeding strategies can mitigate this environmental burden is not always easily predictable. Dynamic mechanistic models of LFS can simulate the effects of feeding strategies on both animal performance and the associated excretion of nutrients. These models are useful tools to produce the life cycle inventories needed in the second step of a life cycle assessment. In the pig-fattening unit, the effects of multiphase group feeding depend largely on the variability of growth potential among pigs and the farmer has decisions that can target either the group of pigs or each individual (e.g., when choosing pigs to deliver to slaughterhouse). Therefore, simulating the growth trajectory of each individual pig and events like deliveries to slaughterhouse makes it possible to estimate the response of the pig population rather than the average pig to feeding strategies. Individual-based models (IBM) are particularly adequate because they make it possible to simulate each individual and to build the response of the population from the aggregation of individuals. Agent-based models are IBM that rely on self-governing agents made of properties, behavioral rules and resources that allow each agent to make decisions upon the occurrence of an event, which can be triggered randomly or not. This hands-on will focus on a specific example dedicated to the simulation of the response (growth, nitrogen excretion and subsequent gaseous emissions from manure) of a population of fattening pigs to a multiphase feeding strategy. In a first step, we will develop a simplified individual-based model of a pig-fattening pen including pigs and the farmer as agents. In a second step, we will improve this model by introducing interactions between these agents. For both steps, we will work with python language using pre-developed jupyter notebooks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".