MétaCan
Menu
Back to cohort
Record W2800863501 · doi:10.5539/ijc.v10n2p85

Generating Cluster Formulas Using The Primary Clusters And The K(n) Parameters

2018· article· en· W2800863501 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
FieldMedicine
TopicBoron Compounds in Chemistry
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryCluster (spacecraft)Transition metalValence (chemistry)Series (stratigraphy)CrystallographyValence electronMetalAtomic physicsComputational chemistryStereochemistryElectronPhysicsQuantum mechanicsOrganic chemistry

Abstract

fetched live from OpenAlex

The formulas of transition metal clusters can be regarded as multiples of their respective PRIMARY CLUSTERS and K(n) parameters. A primary cluster of a transition metal skeletal element can be defined as that cluster of a mono-skeletal element which obeys the 18 electron rule. Such clusters, among others include, Cr(CO)6, Fe(CO)5, Ni(CO)4, and Zn(CO)3 and the respective K values of the skeletal elements are 6, 5, 4 and 3. The selected K(n) series are given and the derived hypothetical golden clusters are given as examples for illustrations. Selected known golden clusters are also found to be multiples of K(n) parameters and the 18 valence fragment cluster, AuL3.5. The graphical representations of a few selected examples of golden clusters are given.

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)0.001
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 score0.084
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.021
GPT teacher head0.303
Teacher spread0.282 · 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