Bibliographic record
Abstract
The project rankings could be impacted in a number of ways, including changes in political landscapes, fluctating graphite prices and exchange rates, or shortage of funding for exploration and development. In addition, an economic deposit (ore reserve) may or may not be delineated, especially when deposit dimensions, product flake size and purity, processing characteristics and logisitcs are taken into account. Furthermore, lab or pilot process test methods may not scale up to meet anticipated yields, flake size distribution or product purity.Each factor receives a maximum score of 10 points, with equal weighting given to each compiled factor. This results in a maximum score of 60 for each listed stock under consideration. Earlier stage explorers may be detrimentally impacted by some of the quantitative factors, however this partially compensates for the increased risk associated with their stage of development and illustrates the dynamics of the graphite space. New entrants in the rankings include IMX Resources Ltd with its Chilalo project in southern Tanzania and Graphite One Resources Inc. with its Graphite Creek project in Alaska. As mentioned above, Ontario Graphite has been excluded from formal ranking, but included in certain charts for comparison
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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.321 | 0.073 |
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".