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Record W4405093051 · doi:10.1021/acs.chas.4c00081

Proceedings of the 2024 Advancing Chemical Safety and Security Education Symposium at the 27th IUPAC International Conference on Chemistry Education

2024· article· en· W4405093051 on OpenAlexaff
Christine Straut, Zakiya R. Skeete, Jun De Andrew Ng, Ashley K. Elgersma, Fakhri Elabbar, Nabil Bader, Ayad A. Abdalla, Ladislava Navratilova, H. Ben Romdhane, Hooi Ling Lee, Diary I. Tofiq, Dunya Ibrahim Saleh, Mostafa Abdoarrahem

Bibliographic record

VenueACS Chemical Health & Safety · 2024
Typearticle
Languageen
FieldChemical Engineering
TopicChemical Safety and Risk Management
Canadian institutionsThe King's University
FundersNational Nuclear Security AdministrationU.S. Department of StateSandia National LaboratoriesU.S. Department of Energy
KeywordsChemical nomenclatureEngineering ethicsChemistryChemical safetyLibrary scienceEngineeringPolitical scienceEngineering physicsComputer scienceOrganic chemistryBiochemical engineering

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide The inaugural Advancing Chemical Safety and Security Education symposium was held at the 27th IUPAC International Conference on Chemistry Education (ICCE2024). Speakers showcased innovative strategies for seamlessly integrating security concepts into established safety programs, addressing specific needs of diverse academic institutions, and evaluating the effectiveness of different pedagogical approaches. This proceedings publication encapsulates insights from 11 oral presentations, 12 poster presentations, and panel discussions including key recommendations for future advancements in educating chemical safety and security education for academic and industry audiences.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.190
Threshold uncertainty score0.635

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0060.001
Open science0.0010.003
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.1900.056

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 head0.006
GPT teacher head0.280
Teacher spread0.274 · 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

Citations5
Published2024
Admission routes1
Has abstractyes

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