Commodity Markets Outlook, July 2016 : From Energy Prices to Food Prices
Bibliographic record
Abstract
Most commodity price indexes rebounded \n in the second quarter of 2016, continuing their upward climb \n from January lows on improved market sentiment and tapering \n supplies. Oil prices jumped by more than a third due to \n supply outages and strong demand. Given this rebound and \n expected reduction in inventories during the second half of \n the year, the crude oil price forecast for 2016 is being \n raised to 43 dollars per barrel (bbl) from 41 dollars per \n bbl in the April assessment, still a 15 percent drop from \n 2015. Metals prices are projected to decline 11 percent in \n 2016, a slightly larger drop than anticipated in April, \n mainly driven by an ongoing surplus in the copper market. \n Agricultural prices for 2016 have been revised slightly \n upwards due to weather patterns in South America, but are \n still expected to register a marginal decline from last \n year. A large upward revision for precious metal prices of \n more than 8 percentage points versus the April assessment \n reflects the increased demand for safe haven assets. For \n 2017, a modest recovery is projected for most commodities as \n demand strengthens and supply tightens. This issue of the \n Commodity Markets Outlook examines the implications of low \n energy prices for food prices. It finds that, given the \n energy-intensive nature of agriculture, high energy prices \n were an important driver of the post-2006 surge in \n agricultural prices. Over 2011-2016, lower energy prices are \n estimated to account for up to one-third of the projected 32 \n percent decline in prices of grains and soybeans.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.049 | 0.056 |
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".