Bibliographic record
Abstract
This chapter analyses overall efforts made thus far to estimate food loss in Canada and identifies the data availability and accessibility issues that surround the measurement and reporting of food loss in the country. It also examines Canada’s efforts and initiatives in establishing scientifically acceptable methods for measuring food loss at different levels. In Canada, private efforts to estimate the magnitude of Food Loss and Waste (FLW) began in 2010 when Value Chain Management International published a report titled “Food Waste in Canada: Opportunities to Increase the Competitiveness of Canada’s Agri-food Sector, While Simultaneously Improving the Environment.” Statistics Canada produces some data sources and works on maintaining some resources that can be used for FLW analysis. Statistics Canada produces the “Food Available in Canada” data series using a variety of sources such as surveys, questionnaires, and other statistical information to estimate food availability per person and food availability per person adjusted for losses on an annual basis.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".