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Record W2805680832 · doi:10.5539/ijc.v10n3p15

The Cluster Valence Electrons (VE) Are Natural Numbers of Clusters Generated by K(N) Parameters: VE and K(N) Are Intertwined

2018· article· en· W2805680832 on OpenAlex
Enos Masheija Rwantale Kiremire

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInternational Journal of Chemistry · 2018
Typearticle
Languageen
FieldMaterials Science
TopicX-ray Diffraction in Crystallography
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryValence (chemistry)Cluster (spacecraft)Valence electronElectronCrystallographyChemical physicsAtomic physicsPhysicsQuantum mechanics

Abstract

fetched live from OpenAlex

The paper presents a highly refined work on broad categorization of clusters using the series method with emphasis on cluster valence content. In this regard, the K(n) parameter plays a crucial role. The K(n) parameters are interrelated and have been utilized to generate a cluster map of clusters. The cluster map of skeletal elements and their clusters can be extended indefinitely. The elements refer to main group and transition metal elements. The inter-conversion of K(n) parameter map into a selected portion of cluster valence electron content map was done indicating the origin of the cluster valence electron numbers which are sometimes associated with certain characteristic geometries. The map indicates that the main group and transition metal elements and their clusters are all interlinked via either the K(n) map or the cluster valence electron content map.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.014
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.252
Teacher spread0.245 · 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