Recent advances in the self-assembly of polynuclear metal–selenium and –tellurium compounds from 14–16 reagents
Bibliographic record
Abstract
Abstract The use of reagents containing bonds between group 14 elements and Se or Te for the self-assembly of polynuclear metal–chalcogen compounds is covered. Background material is briefly reviewed and examples from the literature are highlighted from the period 2007–2017. Emphasis is placed on the different classes of 14–16 precursors and their application in the targeted synthesis of metal–chalcogen compounds. The unique properties arising from the combination of specific 14–16 precursors, metal atoms, and ancillary ligands are also described. Selected examples are chosen to underline the progress in (i) controlled synthesis of heterometallic (ternary) chalcogen clusters, (ii) chalcogen clusters with organic functionalized surfaces, and (iii) crystalline open-framework metal chalcogenides.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".