PDAC 2016: : Juniors trial alternative business models to tempt investors
Bibliographic record
Abstract
Today the market doesn't want to see a preliminary economic assessment (PEA), that's not what's important now, Kiril Mugerman, CEO of TSX-V-listed rare earths developer, GeoMega Resources Inc., told IM. DNI Metals Inc., another Canada-based exploration company, is tackling the graphite market in a different way. DNI is buying graphite from Brazil and processing it, before selling the resulting material to end users in small quantities to build up a customer base for its own graphite project in Madagascar. [Jon Hykawy] cautioned that the 25,000 tonne overlap is not a large margin of error, however. of these [new lithium] projects aren't completely financed and there should be some concern about supply, but not all lithium goes into batteries. Some of it is lower value and used for things like greases and there is some elasticity in market segments, he said.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.121 | 0.030 |
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".