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Record W2949382518 · doi:10.2478/s11532-013-0226-3

Crystalographic and structural characterization of heterometallic platinum compounds. Part III: heterotrinuclear compounds

2013· article· en· W2949382518 on OpenAlexaff
Milan Melnı́k, Peter Mikuš, Clive Ε. Holloway

Bibliographic record

VenueOpen Chemistry · 2013
Typearticle
Languageen
FieldChemistry
TopicOrganometallic Complex Synthesis and Catalysis
Canadian institutionsYork University
Fundersnot available
KeywordsPlatinumTransition metalCrystallographyChemistryCharacterization (materials science)MoleculeDistortion (music)Ligand (biochemistry)MathematicsStereochemistryMaterials scienceNanotechnologyOrganic chemistryReceptorCatalysis

Abstract

fetched live from OpenAlex

Abstract This review article includes over three hundred and sixty heterotrinuclear platinum complexes of the composition Pt2M (205 examples), PtM2 (132 examples) and PtMM (24 examples). The heterometals include the non-transition and transition metals. Three metal atoms form a wide variability of frameworks: M3 triangular, dicapped M3 triangular, V shaped M3, M3 linear, five-, six- and seven- metallocycles and unique structures of which triangular and linear are the most common. This has led to a rich chemistry of platinum not only from variability of metals, but also from their framework and stereochemistry. The shortest Pt-M (non-transition) and Pt-M (transition) bonds are 2.315(1) Å for Pt-Ga and 2.4896(9) Å for Pt-Co. The shortest Pt-Pt bond distance is 2.581(1) Å. Two complexes exist in two isomeric forms and several others contain crystallographically independent molecules. All are typical examples of distortion isomerism. Correlations between structural parameters, heterometal and ligand donor atoms are developed and discussed.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.014
GPT teacher head0.217
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2013
Admission routes1
Has abstractyes

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