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Record W2273930543 · doi:10.5539/ijc.v8n1p109

The Categorization and Structural Prediction of Transition Metal Carbonyl Clusters Using 14n Series Numerical Matrix

2016· article· en· W2273930543 on OpenAlexvenueno aff
Enos Masheija Rwantale Kiremire

Bibliographic record

VenueInternational Journal of Chemistry · 2016
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsnot available
FundersUniversity of Namibia
KeywordsChemistryValence (chemistry)Valence electronSeries (stratigraphy)Cluster (spacecraft)CrystallographyCategorizationElectron transferElectronComputational chemistryPhysical chemistryOrganic chemistryQuantum mechanicsPhysics

Abstract

fetched live from OpenAlex

A matrix table of valence electron content of carbonyl clusters has been created using the 14n-based series. The numbers so generated form an array of series which conform precisely with valence electron contents of carbonyl clusters. The renowned 18 electron rule is a special case of 14n+4 series. Similarly, the 16 electron rule is another special case of the 14n+2 series. Categorization of the carbonyl clusters using the matrix table of series has been demonstrated. The table is so organized that clusters numerically represented can easily be compared and analyzed. The numbers that are diagonally arranged from right to left represent capping series. The row from right to left represents a decrease in valence electron content with increase in cluster linkages. The variation of cluster shapes of constant number of skeletal elements especially four or more may be monitored or compared with the variation with the valence electron content.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.282
Teacher spread0.271 · 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

Citations6
Published2016
Admission routes1
Has abstractyes

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