MétaCan
Menu
Back to cohort
Record W7117259346 · doi:10.1021/acs.chas.5c00189

Enhancing Management of Time-Sensitive Chemicals in Higher Education: A Proactive Approach to Safety and Risk Reduction

2025· article· en· W7117259346 on OpenAlexaff
Janina Willkomm, Johanna Andryszewicz, Eoin P. O'Grady

Bibliographic record

VenueACS Chemical Health & Safety · 2025
Typearticle
Languageen
FieldChemical Engineering
TopicChemical Safety and Risk Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHazardous wasteScope (computer science)Process safety managementKey (lock)HazardPsychological interventionRisk managementLaboratory safetyHazard analysis

Abstract

fetched live from OpenAlex

Time-sensitive chemicals are substances that can develop an explosion hazard when stored for prolonged periods. If not properly managed, they may deteriorate to create unknown hazardous conditions, increasing the risk of fires and explosions. When these chemicals are found under such unsafe conditions, they can no longer be handled safely by lab personnel and are outside the scope of general hazardous material disposal. Their removal requires costly, specialized contractors or high-profile interventions involving fire departments and police services. After a near-miss incident, the UCalgary EHS Lab Safety team prioritized revising the Lab Safety Program to focus on proactively managing time-sensitive chemicals. Key elements of the approach include increased awareness and education, improved guidance and resources, response processes to unsafe items, targeted risk-reduction initiatives, program sustainment, and continuous improvement. By sharing UCalgary’s approach, along with key challenges and lessons learned from campus-wide initiatives, near-miss incidents, and both planned and unplanned specialized contractor disposal events, we hope to support higher education institutions in strengthening their time-sensitive chemical management program.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.488
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.265
Teacher spread0.256 · 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 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
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueACS Chemical Health & SafetySame topicChemical Safety and Risk ManagementFrench-language works237,207