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Record W2480401582 · doi:10.5539/ijc.v8n3p62

The Application of the 4n Series Method to Categorize Metalloboranes

2016· article· en· W2480401582 on OpenAlexvenueno aff
Enos Masheija Rwantale Kiremire

Bibliographic record

VenueInternational Journal of Chemistry · 2016
Typearticle
Languageen
FieldMedicine
TopicBoron Compounds in Chemistry
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryBoranesSeries (stratigraphy)StereochemistryCrystallographyBoronOrganic chemistry

Abstract

fetched live from OpenAlex

Metalloboranes have been categorized by using 4n series method. More than one hundred metalloboranes have been analyzed and characterized by the method. The clusters have been found to be centered within the series range S = 4n+6 to 4n-8. According to the classification, the clusters of series S= 4n+0 are mono-capped, S = 4n-2, bi-capped, S = 4n-4, tri-capped, S = 4n-6, tetra-capped and S= 4n-8, penta-capped. Whereas the known stable boranes have almost non-existent capped boranes, such clusters are prevalent within metalloborane complexes. This implies that metal fragments have the potency of stabilizing the fragile capped boranes.

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)0.001
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: none
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.002

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 head0.323
Teacher spread0.313 · 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

Citations15
Published2016
Admission routes1
Has abstractyes

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