Bibliographic record
Abstract
In terms of issues, those questioned by IM said that the only conceivable hurdle is the amount of regulation that the TSX and TSX-V require in order to list. While some say that this has cleaned up mining The days of the'wild west' Vancouver stock exchange have been cleaned up and replaced by new regulations governing how information is accurately disseminated to the public, [Peter Cunningham] told IM others complain that the regulations can be for smaller companies. TSX-V is a good market, but costs of'compliance' regulations can be onerous for a small company, Alex Harris, Newfoundland Fluorspar Exploration (Newfluorex) company president, told IM. Image: FednavSo, legal firms, accounting firms, environmental firms, engineering firms and brokerages that all deal in mining, all exist within walking distance of each other in downtown Toronto and Vancouver. You're accessing an exchange, yes, but you're also accessing a very experienced mining cluster and I think it's quite unique in the world, he said.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.340 | 0.085 |
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".