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Record W2025434488 · doi:10.1021/om030071h

Potential Energy Surfaces in Transition States for Associative Reactions of Metal Carbonyl Clusters:  Reactions of Rh<sub>4</sub>(CO)<sub>12</sub> with P-Donor Nucleophiles

2003· article· en· W2025434488 on OpenAlexaff
Kevin A. Bunten, David H. Farrar, Anthony J. Poë

Bibliographic record

VenueOrganometallics · 2003
Typearticle
Languageen
FieldChemistry
TopicOrganometallic Complex Synthesis and Catalysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsChemistryNucleophileTransition metalTransition stateMetalPhysical chemistryComputational chemistryCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

The metal carbonyl cluster Rh 4 (CO) 12 reacts with a wide variety of P-donor nucleophiles solely by an associative mechanism, and the rate constants can be analyzed quantitatively according to the electronic and steric properties of the nucleophiles by the QALE method. An unexpected outcome of this analysis is that inclusion of what has become known as the “aryl effect” is clearly necessary, together with positive contributions to the rates of effects due to the π-acidity of the phosphite nucleophiles. A simple way of representing the individual contributions of the various effects to the overall rates is described, and the general σ-basicity and steric effects can be represented graphically by a three-dimensional “log k 2 surface” upon which the aryl and π-acidity effects can separately be superimposed as additional peaks. A free energy surface can be obtained by a simple scale change.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.010
GPT teacher head0.207
Teacher spread0.197 · 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 designSimulation or modeling
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

Citations13
Published2003
Admission routes1
Has abstractyes

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