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Record W4293727230 · doi:10.1021/acs.jafc.2c00965

Resilience of Chemistry: An Introduction to the Agricultural and Food Chemistry Technical Program at the 262nd American Chemical Society National Hybrid Meeting & Exposition, Both Online and in Atlanta, Georgia

2022· article· en· W4293727230 on OpenAlexaff
Michael Appell, Charles L. Cantrell, Jungeun Cho, Hak-Jong Choi, Hyang-Sook Chun, Laurel A. Doherty, Michael Granvogl, J. Philip Karl, Youngmok Kim, Masuko Kobori, Kwang‐Geun Lee, LinShu Liu, Xiaonan Lu, Karley K. Mahalak, M. J. Morello, Hiroshi Nabetani, Helen L. Ngo, Bosoon Park, Majher I. Sarker, Jason W. Soares, Michael H. Tunick, Madhav P. Yadav, Wally Yokoyama, Guodong Zhang, Valtcho D. Zheljazkov

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2022
Typearticle
Languageen
FieldChemical Engineering
TopicChemical Safety and Risk Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAtlantaLibrary scienceAttendanceAgricultureChemistryPolitical scienceMedical educationMedicineMetropolitan areaComputer scienceHistoryLawArchaeology

Abstract

fetched live from OpenAlex

) based on the Agricultural and Food Chemistry Division (AGFD) technical program, at the 262nd American Chemical Society National Meeting. This was the first national meeting held in a hybrid format, both virtually and in-person in Atlanta, Georgia, U.S.A., on August 22-26, 2021. The AGFD proudly hosted 12 symposia, including three award symposia. There were 34 sessions held in total, with 143 oral presentations and 49 poster presentations. This meeting was highly successful in terms of attendance, and technology issues experienced at the previous virtual meetings were successfully resolved.

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)0.002
metaresearch head score (Gemma)0.001
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: Other · Consensus signal: Other
Teacher disagreement score0.102
Threshold uncertainty score0.341

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.1020.049

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 head0.005
GPT teacher head0.212
Teacher spread0.206 · 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
GenreOther

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

Citations1
Published2022
Admission routes1
Has abstractyes

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