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Record W4241551594 · doi:10.32920/ryerson.14662152.v1

Flight simulator system for research and teaching application

2021· preprint· en· W4241551594 on OpenAlexaboutno aff
Siju Thomas

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicUrban Transport and Accessibility
Canadian institutionsnot available
Fundersnot available
KeywordsWindsorEnvironmental scienceAir pollutionGeographyMeteorologyWind directionEnvironmental healthClimatologyWind speedMedicineGeology

Abstract

fetched live from OpenAlex

A spatial analysis of smog events in Southern Ontario and prevailing winds reveals various patterns that occur during smog advisories. Smog events cause numerous excess deaths and illnesses each year throughout Southern Ontario due to high levels of air pollutants that are generated in North America. Cardiovascular and respiratory illnesses are the main hospital admissions that occur during summer smog episodes. These effects are experienced throughout regions located along the Windsor-Quebec corridor, but there are variations in the numbers of affected people due to the effects of surrounding geographical features and the local contribution of air contaminants. Meteorological differences play a major role in the effects of smog events with factors such as temperature and prevailing winds. This study examines the effects of long distance transport of contaminants from origins in the United States into Canada as indicated by respiratory and cardiovascular mortality and morbidity effects during 9 smog events. This study found that during certain conditions there is a correlation between wind direction and smog related mortality and morbidity.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.448
Threshold uncertainty score0.787

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.4480.238

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 head0.110
GPT teacher head0.445
Teacher spread0.335 · 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.

Study designBench or experimental
Domainnot available
GenreMethods

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
Published2021
Admission routes1
Has abstractyes

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