Bibliographic record
Abstract
Food loss and waste has gained international attention for its negative environmental, economic, and social repercussions. In Canada approximately 58% of all the food produced and imported for human consumption goes uneaten each year (Gooch et al. 2019, 23). Provincial, territorial, and municipalgovernments have begun to develop and implement strategies to reduce food loss and waste in their jurisdictions. To track the impact of these strategies, it is necessary to know much food loss and wasteexists in a jurisdiction to create a baseline and measure reductions against this number. Unfortunately, not much is currently known about how food loss and waste is monitored and measured in Canada as it varies significantly throughout the agri-food system and across different governments. This presentationreports on preliminary findings on a research project that utilizes interviews with key stakeholders (including government policy makers, food business executives, food security organization leaders, and food waste consultants) to gain insight into these various, differing procedures. This research aims toassist government policy makers and the agri-food business community in adopting and/or improvingtheir monitoring and measuring procedures within their jurisdiction, industry, and/or individualbusinesses. Funding: OMAFRA through the Ontario Agri-Food Innovation Alliance (HQP Program) & Arrell Food Institute
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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".