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Record W4386034573 · doi:10.1016/j.xcrp.2023.101548

Base metal chemistry and catalysis

2023· article· en· W4386034573 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueCell Reports Physical Science · 2023
Typearticle
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaAmerican Chemical Society Petroleum Research FundCanada Foundation for InnovationCouncil of Ontario UniversitiesUniversity of Windsor
KeywordsCatalysisBase metalChemistryMetalBase (topology)Organic chemistryMaterials scienceMetallurgyMathematics

Abstract

fetched live from OpenAlex

This perspective provides an entry-level conversation concerning base metal catalysis as a green and sustainable solution in industrial and academic contexts. We establish a definition of “base metal,” challenging readers to consider the ethical implications of metal sourcing. We explore what it means to be “sustainable” and provide information on current efforts in synthetic chemistry. We provide examples of current catalytic trends and transformations in popular fields such as cross-coupling and small-molecule conversion, highlighting relevant base metal systems. Finally, we consider social context—for example, decisions related to catalyst development are often driven by factors including costliness, safety, social adoptability (whether society will accept its usage), and performance. How do we move base metal catalysis to the forefront? Is society concerned if materials are fabricated from cheaper and more abundant sources? How does the synthetic chemistry community guide this knowledge translation?

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 score0.031
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.009
GPT teacher head0.247
Teacher spread0.238 · 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