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Record W4393579978 · doi:10.5281/zenodo.6994780

Electronic Supporting Information for Catalytic Ammonia Oxidation to Dinitrogen by a Nickel Complex

2022· dataset· en· W4393579978 on OpenAlexaff
Róbert K. Szilágyi, David N. Stephens, Michael T. Mock

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2022
Typedataset
Languageen
FieldChemical Engineering
TopicAmmonia Synthesis and Nitrogen Reduction
Canadian institutionsUniversity of British Columbia, Okanagan Campus
Fundersnot available
KeywordsNickelCatalysisAmmoniaChemistryInorganic chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The dataset provides electronic supporting information in the format of XYZ molecular files, formatted Gaussian checkpoint files, and cube files for atomic spin density distributions for selected complexes obtained while investigating the catalytic mechanism of ammonia oxidation to dinitrogen using a N-heterocyclic carbene containing nickelocene complex. The level of theory used for all calculations is omega-B97xD with def2TZVP basis set. All calculations were performed using the Gaussian16 suite of programmes. Model Set 1 contains the metal free compounds and were used to calculate the overall thermodynamics of the ammonia oxidation reaction. Model Set 2 corresponds to the most truncated, in vacuo optimized structures. Model Set 3 comprises from non-truncated, realistic structures embedded in polarizable continuum model of benzene.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.197
Threshold uncertainty score0.658

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.1970.102

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 head0.022
GPT teacher head0.248
Teacher spread0.226 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

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