Bibliographic record
Abstract
The wide variety of metal sulfide structures and their accommodation of atomic substitution, non-stoichiometry and metal-metal (M-M) and ligand-ligand interactions allows for diverse physical, chemical and electronic properties. The energy band structure of 3d transition-metal sulfides, in particular, is strongly influenced by the covalence of metal-S bonds, which results in hybridization of S 3p and metal 3d bonding states and direct or indirect M-M bonding interactions in favorable cases. Differences in the phase relations of isostructural metal sulfides are often attributable to subtle changes in electronic states. The literature on metal sulfide phase relations relevant to the earth sciences is very extensive and could not possibly be summarized in a single chapter. Therefore, following Craig and Scott (1974), this chapter focuses on the base metal (Fe, Co, Ni, Cu, and Zn) sulfides, with the literature for other metal chalcogenides and pnictides and some sulfosalts summarized in a single table (Table 1⇓). The relevant phase relations of the platinum-group element (PGE) chalcogenides and pnictides have been comprehensively reviewed in Makovicky (2002). A section on the halite structure sulfides (niningerite, alabandite and oldhamite) is also included in this chapter. The material presented here relates closely to that discussed in other chapters, in particular the chapter on sulfide thermochemistry (Sack and Ebel 2006). The importance of understanding phase equilibria in the context of electronic and magnetic properties and, hence, electronic structure is also emphasized, thereby reinforcing material presented in several other chapters (Pearce et al. 2006; Vaughan and Rosso 2006).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".