Development of a life cycle impact assessment methodology for animal welfare with an application in the poultry industry
Bibliographic record
Abstract
To date, assessment of animal welfare impacts remains largely unconsidered in life cycle assessment (LCA). Two previous attempts have been made to integrate animal welfare assessment into the LCA framework, both of which are insufficient in their coverage of the numerous factors contributing to animal welfare impacts. Here, a novel type 1 (i.e., reference scale) life cycle impact assessment method is proposed for animal welfare assessment of laying hens. This includes identification of all requisite components of a life cycle impact assessment method (i.e., area of protection, stakeholder and impact categories, impact subcategories, inventory indicators and data requirements, and characterization factors) based on a review of the animal welfare literature, in line with best practices in both the animal welfare science, and life cycle assessment fields. The proposed method is subsequently tested using a case study of the Canadian egg industry, and levels of relative risk for different impact subcategories related to animal biological health, behaviour, and affective state are calculated. This method provides results in line with expectations based on the animal welfare literature. Further, the process used for development of this method is generalizable, and may be applied to development of similar methods for assessment of other livestock species, as the area of protection, stakeholder and impact categories, and impact subcategories are not species specific. This method improves upon previous efforts to incorporate animal welfare assessment into the LCA framework. Continued improvement is necessary however, particularly with respect to incorporation of additional hen life cycle stages, modeling of affective state and positive welfare contributions, and uncertainty assessment. Continued development of animal welfare LCIA methods is necessary given the growing status of animal welfare as an issue of concern worldwide, and to ensure net-positive sustainability outcomes in food 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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".