Rank and file: : Assessing graphite projects on credentials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it