Bibliographic record
Abstract
[...]while investors are still wary of high risk investments, the strong performances of mining shares on global stock exchanges suggest that equity markets are confident the recovery in commodities demand is sustainable. Figures for Canada's Toronto Venture Exchange (TSX-V), the world's biggest exchange for junior mining equities on which around half of all the volume traded is made up of mining stocks, show a 35.6% year on year increase in total financings raised to Canadian dollar (C$) 4.6bn ($3.5bn*) during the first nine months of 2016. In December, the S&P/ASX 300 Metals and Mining Index on the Australian Securities Exchange (ASX), the second largest market in the world for mining listings, achieved its highest intraday value since 2014 and climbed by 53% over the course of 2016, its first annual rise since 2010. At the Mines and Money London Conference in December 2012, fund managers confidently predicted that a shortage of capital would lead to a wave of consolidation in the mining industry. [...]M&A in the sector has practically stalled. First phase drilling at Savannah's Mozambique mineral sands project, which is a JV with Rio Tinto - one of the few deals struck between a junior and a major since the collapse in mining investment.
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.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.032 | 0.013 |
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".