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Record W4241619089 · doi:10.26434/chemrxiv.14454585.v1

A Mechanistic Investigation of the Suzuki Polycondensation Reaction Using MS/MS Methods

2021· preprint· en· W4241619089 on OpenAlex
Michelle Ting, Lars P. E. Yunker, Ian C. Chagunda, Katherine Hatlelid, Meghan Vieweg, J. Scott McIndoe

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

VenueChemRxiv · 2021
Typepreprint
Languageen
FieldEngineering
TopicIon-surface interactions and analysis
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Victoria
KeywordsCatalysisCondensation polymerChemistryMonomerPolymerizationDecompositionCombinatorial chemistryPolymerPolymer chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Understanding catalytic reactions is inherently difficult because not only is the catalyst the least abundant component in the mixture, but it also takes many different forms as the reaction proceeds. Precatalyst is converted into active catalyst, short-lived intermediates, resting states, and decomposition products. Polymerization catalysis is harder yet to study, because as the polymer grows the identities of these species change with every turnover as monomers are added to the chain. Modern mass spectrometric methods have proved to be up to the challenge, with multiple reaction monitoring (MRM) in conjunction with pressurized sample infusion (PSI) used to continuously probe all stages of the Suzuki polycondensation (SPC) reaction. Initiation, propagation, and termination steps were tracked in real time, and the outstanding sensitivity and low signal-to-noise of the approach has real promise with respect to the depth with which this reaction and others like it can be studied.

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 score0.224
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.039
GPT teacher head0.300
Teacher spread0.261 · 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