Measurement of carbon monoxide concentration levels within an underground parking lot throughout the day
Bibliographic record
Abstract
Background: During the fall and winter months, people opt to using cars as a mode of transportation to and from work, school, or recreation. The ease of access, comfort, and efficiency of travel prompt an increase in drivers. Underground lots are ideal parking spaces during these months, which see an increase in traffic and subsequent rise in emissions, specifically carbon monoxide (CO) that can be hazardous to health at certain concentrations. This study is to determine the levels of CO in a confined parking space Methods: Air quality and composition were determined via passive dosi-tubes that were affixed onto columns within the Langara College underground parking lot in the morning and picked up for analysis in the afternoon. Results: There is an increase in carbon monoxide concentration within the underground parking lot, during peak hours. Traffic within the lot is found to be higher during poor weather conditions which correlate with ease of use and comfort of driving a car. There is also an increase in traffic on Tuesdays and Thursdays, which is likely dictated by class times. Carbon monoxide levels did not fail to meet government regulations during any sampling period. Conclusions: The air composition in the Langara underground parking lot is safe even during periods of high traffic, for the average person. However, individuals with underlying medical conditions should enter with caution, as the recorded CO levels can aggravate pre-existing cardio-pulmonary diseases.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".