Bibliographic record
Abstract
World agricultural prices in a historical perspectiveWorld agricultural prices are very volatile which is due to traditional characteristics of agricultural markets such as inelastic (short run) supply and demand curves (see, Meijl et al. 2003).13 The volatility is also high because the world market is a relatively small residual market in a world distorted by agricultural policies.14 The combination of high technological change and inelastic demand cause real world prices to decline in the long run (trend).The prices, however, of many (major) agricultural commodities have risen quickly over recent years (see Figure 1).Recent increase in agricultural prices are strong, but even with the increase that we have observed in the last three years, real agricultural prices are still low compared to the peaks in prices of the mid-70s.Local prices are linked with these world prices.The transmission effect depends on the transparency of markets, market power and accessibility 13 'World food prices are instable and will remain unstable in the future.Forecast errors are large in predictions of world prices.There are always unexpected events in important drivers such as yields which are dependent on weather, plagues and diseases' (See Van Meijl et al., 2003).14 Trade share (2006) in global production: rice (7%), cheese (7%), coarse grains (11%) and wheat (20%), FAO Statistics.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".