Canadian Minerologist Vol. 25, pp.237-244 (1987) EFFECT OF SUBSTITUTIONS ON THE CELL DIMENSION OF TETRAHEDRITE
Bibliographic record
Abstract
Linear regression analyses of published data for tetra-hedrite define equations that predict the cell dimension of natural and synthetic tetranedrite, in terms of compositions [r atoms per formula unit, go withiu an average of t 0.02 A, The equations are: a(A)our*.s = 10.379+0.082(A9)-0.01 (Ag|-0.009(Cu*) + 0.66(Hg)-0.038(As) + 0. 144(Bi) and d(A),yoti,"ti " = 10.381 + 0.039(Ag) + 0.003(Agz)-0.019(Cu*) + 0.064(CdF0.037(As) for natural and synthetic tetrahedrites, respectively, where Ag, Hg, As, Bi and Cd are the number of atoms per formula unit, and Cu * is equal to 2.0-(Fe+Zn+Hg+Cd). The equations have correlation coefficients R2 of 0.9789 and 0.9893, respectively, and all of the regression coefficients 0 are significant at the 9590 confidence level. Variation of the Fe/Zn ratio has no appreciable effect on a. Calculation of changes of molar volume with composition indicate that As*Sb, Bi*Sb,
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.142 | 0.039 |
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".