Chlorine Partitioning and Atmospheric Measurements During Continental Winter: Gas and particle-phase data from Toronto, Canada (2019)
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.
Bibliographic record
Abstract
This dataset contains high-resolution gas-phase and particle-phase atmospheric measurements collected during late winter and early spring 2019 at the York University Air Quality Research Station in Toronto, Ontario, Canada. The data were gathered to investigate the partitioning and behavior of reactive chlorine under cold urban conditions and support the study “Exploring the Relationship Between Particle and Gas Phase Chlorine in Continental Winter” (Angelucci et al., 2025). Gas-phase measurements include hydrogen chloride (HCl) at 0.5 Hz frequency using a cavityring-down spectrometer (CRDS), along with nitrogen oxides (NO, NO₂) and ozone (O₃) recorded every 5 minutes. Particle-phase data were collected using a nano-MOUDI impactor and analyzed via ion chromatography to provide size-resolved ionic composition across 12 aerodynamic diameter bins. The dataset also includes supporting meteorological parameters (temperature, relative humidity, wind speed and direction), solar irradiance, and PM1 and PM10 mass concentrations derived from SMPS and TEOM instruments, respectively. All data streams were quality-controlled with rigorous calibration, blank corrections, and synchronization across instruments. The files are time-resolved and cross-referenced, enabling integrated analysis of chlorine speciation, secondary aerosol formation, and environmental conditions typical of continental winters in urban North America. This dataset is suitable for atmospheric chemists, air quality modelers, and researchers investigating halogen chemistry, urban pollution, and cold-season atmospheric processes.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it