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Record W4220704719 · doi:10.1002/cjce.24408

Support for catalysis in <scp>C</scp>anada by the <scp>CIC C</scp>atalysis <scp>D</scp>ivision and <scp>C</scp>anadian <scp>C</scp>atalysis <scp>F</scp>oundation

2022· article· en· W4220704719 on OpenAlexaffvenueabout
Bryce McGarvey, Natalia Semagina, Josephine M. Hill

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsUniversity of CalgaryUniversity of AlbertaImperial Oil (Canada)
Fundersnot available
KeywordsCatalysisPipeline (software)Political scienceEngineeringChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract 2020 and 2021 marked major milestones for the catalysis community in Canada. The Catalysis Division of The Chemical Institute of Canada marked its 50‐year anniversary in 2021, and the Canadian Catalysis Foundation celebrated its 30th anniversary in 2020. Both organizations have been instrumental in supporting and advancing the broad and evolving field of catalysis in Canada. This article summarizes the genesis of these organizations and highlights the key roles that they serve for the catalysis community. The health and vibrancy of the catalysis community in 2022 is a testament to the vision, insights, and efforts of those in the community in the 1960s and 1970s who created a framework that helped to bridge the gaps between science and engineering in the field of catalysis. The establishment of the Division in 1971 pulled together a critical mass of people with common professional interests in all aspects of catalysis and catalyst technology. The division structure facilitated collaborations, formalizing and organizing biennial catalysis symposia, administering award programs, and, now, acting as a pipeline for members and directors of the Canadian Catalysis Foundation.

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 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)0.009
metaresearch head score (Gemma)0.044
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity
Consensus categoriesMeta-epidemiology (narrow)
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 score0.310
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.044
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0020.004
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0060.001
Research integrity0.0010.003
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 head0.210
Teacher spread0.203 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2022
Admission routes3
Has abstractyes

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