Enhancing Food Price Forecasts in Canada: An Integration ofExpert-Driven Covariates and Advanced ML Approaches
Bibliographic record
Abstract
This study addresses the growing issue of food affordability in Canada, exacerbated by recent inflation and other global factors. Canada's Food Price Report (CPFR) is an annual publication that predicts food inflation over the next calendar year. While in recent years the CFPR has leveraged machine-learning (ML), the 2024 report also included a human-in-the-loop approach. This approach included expert-driven economic and climate variables as additional model inputs, with results suggesting that these variables improved forecast accuracy for several food categories. In the present study, we investigate sensitivity of models used for the CFPR report to specific combinations of these covariates. Our preliminary findings suggest potential synergistic effects of combining various covariates, resulting in more accurate forecasts that continue to perform well in changing global conditions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".