Commodity Markets Outlook, April 2016 : Resource Development in an Era of Cheap Commodities
Bibliographic record
Abstract
Most commodity price indexes rebounded in February-March from their January lows on improved market sentiment \nand a weakening dollar. Still, average prices for the first quarter fell compared to the last quarter of 2015, with energy \nprices down 21 percent and non-energy prices lower by 2 percent. Given the recent rebound in oil prices and expected \nsupply tightening in the second half of the year, the crude oil price forecast for 2016 has been raised to $41 per barrel \n(bbl), up from $37/bbl in the January assessment (and represents a drop of 19 percent from 2015.) Metals prices are \nprojected to decline 8 percent, a slightly smaller drop than anticipated in January due to supply reductions. Agricultural \nprices have been revised marginally lower on signs of adequate harvests in major producers, and are expected to \nregister a decline of 4 percent from last year. Looking to 2017, a modest price recovery is projected for most commodities \nas demand strengthens. Crude oil is projected to rise to $50/bbl as the market moves into balance. This issue of the \nCommodity Markets Outlook examines the implications of resource development in an era of lower commodity prices \nand concludes that ambitious improvements in governance and sounder macroeconomic policies are required to mitigate \ndelays and risks.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.015 |
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".