Agricultural commodities and climate change
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The agricultural commodity market is sensitive to variations in weather and climate, which can disrupt supply and cause price fluctuations. Some of the key positive and negative impacts of climate change on agricultural commodities, using the examples of wheat and barley, are identified; of particular significance are temperature changes, water availability, and CO2 fertilization. Although they are not exempt from the negative impacts of climate change, higher latitude regions of production, including Canada and Russia, will benefit the most from climate change. The impacts on other important production regions, such as parts of Europe, the US, and Argentina, will be more mixed. Market stability in all regions will also be affected by changes in climate and weather extremes. To increase resilience to the effects of weather events and climate change on the agricultural commodity market, countries should diversify their sources of supply, encourage more countries to grow and export the relevant commodities, and support crop research and climate adaptation. Policy relevance Climate change will substantially affect future food security and the price of agricultural commodities. This study takes a broad approach to identify the key aspects of the agricultural commodities market that are vulnerable to climate change and suggests ways in which policy makers might improve its resilience.
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.
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 | 0.001 |
| 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 it