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Record W2799681608 · doi:10.1080/10773525.2018.1468130

Exposure assessment of non-electric ice resurfacer operators in indoor ice rinks: a pilot study

2017· article· en· W2799681608 on OpenAlex

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

VenueInternational Journal of Occupational and Environmental Health · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicLife Cycle Costing Analysis
Canadian institutionsToronto Metropolitan University
FundersRyerson University
KeywordsEnvironmental scienceIce creamFood scienceChemistry

Abstract

fetched live from OpenAlex

Exposure of ice resurfacer operators to indoor air contaminants was measured in six indoor ice arenas. A standardized questionnaire on technical and operational features was employed and indoor airborne concentrations of carbon monoxide (CO), carbon dioxide (CO2), nitric oxide (NO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and total volatile organic compounds (VOCs) were measured. Air samples were collected using a range of direct reading instruments attached to the driver’s seat of the resurfacer. The range of mean exposure concentrations via positional sampling (i.e. as close as able to the operator’s breathing zone) were 5.7–7.4 ppm, 694–2171 ppm, <0.5 to 0.5 ppm, and < 0.1 to 0.2 ppm, for CO, CO2, NO, and NO2, respectively. Exposure levels for SO2 and VOC were below detection. Overall, each of the measured indoor air contaminants was found to be below its respective occupational exposure limits (OEL), suggesting that the risk of hazardous exposure is low. The use of natural gas as a fuel source is believed to contribute to low contaminant concentrations.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.038
GPT teacher head0.346
Teacher spread0.308 · 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