Bibliographic record
Abstract
Abstract The reactivity of the chalcogen–nitrogen bond toward main-group element or transition-metal halides, as well as electrophilic and nucleophilic reagents, is the source of a variety of applications of Se–N and Te–N compounds in both inorganic or organic chemistry. The thermal lability of Se–N compounds also engenders useful transformations including the formation of radicals via homolytic Se–N bond cleavage. These aspects of Se–N and Te–N chemistry will be illustrated with examples from the reactions of the binary selenium nitride Se4N4, selenium–nitrogen halides [N(SeCln)2]+ (n = 1, 2), the synthons E(NSO)2 (E = Se, Te), chalcogen–nitrogen–silicon reagents, chalcogen(IV) diimides RN=E=NR, the triimidotellurite dianion [Te(NtBu)3]2−, chalcogen(II) amides and diamides E(NR2)2 (E = Se, Te; R = alkyl, SiMe3), and heterocyclic systems.
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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".