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Record W4399733483 · doi:10.1021/cen-10218-reactions

Reactions

2024· article· en· W4399733483 on OpenAlexaboutno aff

Bibliographic record

VenueC&EN Global Enterprise · 2024
Typearticle
Languageen
FieldChemistry
TopicVarious Chemistry Research Topics
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Letters to the editor Scientists playing guitar Thanks C&EN for this brief article highlighting Carolyn Bertozzi’s bass guitar passion (May 6, 2024, page 40)! I am currently working up a presentation “Music Is Dope(amine)”—maybe a TEDx talk somewhere someday—about how music operates and stimulates the same creative centers of the brain as many of our deeply advanced technical endeavors. While in chemistry graduate school at the University of Waterloo in Ontario in the late 1970s, country rock was quite in vogue. I took part of my scholarship funds and purchased a student-model pedal steel guitar, more of a musical machine than instrument. The pleasure and challenge of its complexity still so stimulate my brain that even now in my later 60s, I secure the occasional weekend gig in the Detroit area (now with professional-level and quality instruments). I have also played some Fender basses, as Bertozzi, but I now

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.773
Threshold uncertainty score0.998

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)0.0030.001

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 head0.303
Teacher spread0.293 · 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 teacher head, not a consensus.

Study designNot applicable
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
Published2024
Admission routes1
Has abstractyes

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