Distribution of food waste and losses in Latin America and the Caribbean in terms of calories and proteins, 2021-2023
Bibliographic record
Abstract
The OECD-FAO Agricultural Outlook 2024-2033 provides a consensus assessment of the ten-year prospects for agricultural commodity and fish markets. This Outlook edition reveals important trends. Emerging economies will be pivotal in shaping the global agricultural landscape, with India expected to overtake China as the leading player. Yet calorie intake growth in low-income countries is projected to be only 4%. Agriculture's global greenhouse gas intensity is projected to decline, although direct emissions from agriculture will likely increase by 5%. If food loss and waste could be halved, however, this would have the potential to reduce both global agricultural GHG emissions by 4% and the number of undernourished people by 153 million by 2030. Well-functioning international agricultural commodity markets will remain vital for global food security and rural livelihoods. Expected developments should keep real international reference prices on a slightly declining trend over the next ten years, although environmental, social, geopolitical, and economic factors could significantly alter these projections.More information can be found at www.agri-outlook.org.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".