Bibliographic record
Abstract
''The question was whether, as Professor [Robert] Bates put it, 'the IM Congress of 1974 will become the first of a series - or go down in a blaze of glory as a one-time operation,''' the coverage of the first Congress mused. Selling natural graphite is not easy, especially when selling a new graphite mine's full capacity of all the different qualities it produces. [Asbury Carbons] signed an exclusive sales and marketing agreement in 1988 with the original investors of the 'Stratmin Graphite Mine' in Lac-des-lles, Quebec, Canada and closed our smaller graphite mine 'Graphite Asbury Quebec'. Going back to 2006 when we restarted Sierra Rutile, we saw very flat pricing until 2010 brought about by legacy contracts. By the end of 2011, prices had more than doubled compared to 2010 and at the start of 2012 they doubled again. Prices continued to appreciate until mid-2012 and then gradually drifted down to the levels we see today. However these levels are still considerably higher than those seen at the end of the legacy contract ''era''.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.236 | 0.099 |
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".