Characterization and Management of Food Loss and Waste in North America
Bibliographic record
Abstract
Policies and programs on food loss and waste (FLW) are gaining momentum across North America as awareness of the issue continues to grow. The Commission for Environmental Cooperation (CEC) established the North American Initiative on Food Waste Reduction and Recovery as part of its Green Economy and Climate Change project areas. This white paper characterizes FLW in Canada, Mexico and the United States and identifies opportunities for the industrial, commercial and institutional (ICI) sector, governments, and nongovernmental organizations (NGOs) to take action across the three countries. The scope of this research included post-harvest to pre-consumer stages of the food supply chain (i.e., post-harvest food production; processing; distribution; retail; and food service). Pre-harvest food production and the consumer stages of the food supply chain are beyond the scope of this study. This project complements the CEC's North American Initiative on Organic Waste Diversion and Processing, which examines composting, anaerobic digestion, and other industrial processes (e.g. rendering, biofuel) for FLW and other organic waste. The content of this white paper was compiled from primary and secondary sources of information in Canada, Mexico, the United States and countries outside of North America. Primary sources included interviews and email exchanges with 167 stakeholders representing various locations, organization types and sizes, and stages of the food supply chain. Secondary sources included reports, white papers, academic papers, news articles, media recordings and government databases, as well as a review of on-the-ground programs and projects implemented by the ICI sector, governments and NGOs. North American and international experts on the subject matter also vetted key findings during a three-day stakeholder session held in Canada, in February 2017.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".