Evaluation of the sustainability of contrasted pig farming systems: integrated evaluation
Bibliographic record
Abstract
The aim of this paper is to present an approach for an integrated evaluation of the sustainability of pig farming systems, taking into account the three classical pillars: economy, environment and society. Eight sustainability themes were considered: Animal Welfare (AW), Animal Health (AH), Breeding Programmes (BP), Environment (EN), Meat Safety (MS), Market Conformity (MC), Economy (EC) and Working Conditions (WC). A total of 37 primary indicators were identified and used for the evaluation of 15 much contrasted pig farming systems in five EU countries. The results show that the eight themes were not redundant and all contributed to the observed variation between systems. The tool was very robust for highlighting the strengths and weaknesses of the systems along the eight themes that were considered. The number of primary indicators could be reduced from 37 to 18 with limited impact on the strengths/weaknesses profile of the individual systems. Integrating the eight theme evaluations into a single sustainability score is based on hypotheses or presumptions on the relative weights that should be given to the eight themes, which are very dependent on the context and on the purpose of the users of the tool. Therefore, the present paper does not have the ambition to provide a ready-for-use tool, rather to suggest an approach for the integrated evaluation of the sustainability of pig farming 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 | 0.042 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".