The outlook for diamond prices is far from glittering
Bibliographic record
Abstract
Significance India’s monetary reform in November 2016 severely affected liquidity conditions in the midstream segment, which is dominated by family-run firms relying on credit to purchase rough stones from miners. India represents only 8% of demand for rough diamonds but is the key centre for stone cutting and polishing, responsible for over 70% of all final product. The market has also been affected by the commissioning of several new mines and the withdrawal of financing by Antwerp Diamond Bank and Standard Chartered. Impacts Nearly one-third of rough stones have a degree of fluorescence; this will continue to attract larger discounts, particularly in Canada. Belgian bank KBC seeks to recover 26 million euros in unpaid loans, seizing assets from a cutter-polisher in Antwerp; defaults may rise. Having placed Ghaghoo mine in Botswana on care and maintenance, Gem Diamonds has now put the operation up for sale. Two recent bankruptcies in India could further cloud banks’ commitment to the sector.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.083 | 0.038 |
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".