Bibliographic record
Abstract
Datasets for "Comparison of the LEO and CPMA-SP2 techniques for black-carbon mixing-state measurements" Paper Details Title: Comparison of the LEO and CPMA-SP2 techniques for black-carbon mixing-state measurements Authors: Arash Naseri, Joel C. Corbin, Jason S. Olfert Affiliation: Department of Mechanical Engineering, University of Alberta, Edmonton, Alberta, Canada Metrology Research Centre, National Research Council Canada, Ottawa, Ontario, Canada Correspondence: Arash Naseri (arash@ualberta.ca) Accepted: 2 April 2024 Published: [Date] Purpose The purpose of providing these datasets is to enhance clarity and reproducibility of the results presented in the paper. By making the data available, we aim to facilitate further research, enable verification of our findings, and support transparency in the scientific process. Measurement Details Location: Kamloops, British Columbia, Canada (50°39'58.4"N, 120°21'45.5"W) Nearby Air Quality Station: 2.2 km away Nearby Highway: 0.7 km from the Trans-Canada highway Experiment Dates: 21 and 22 July 2021 Dataset Descriptions Case I Time Period: 21 July, 11:54 to 13:44 LT PM2.5 Concentrations: 1.7 to 4 μg/m³ Description: Low concentrations of mostly thinly coated rBC particles, presumably due to urban and highway emissions, and possibly some wildfire smoke. Case II Time Period: 22 July, 11:31 to 12:41 LT PM2.5 Concentrations: 10 to 81 μg/m³ Description: Moderate concentrations with a mixture of thinly and thickly coated rBC particles due to urban emissions and nearby forest fires. Case III Time Period: 22 July, 10:23 to 11:29 LT PM2.5 Concentrations: 122 to 104 μg/m³ Description: High concentrations with mostly thickly coated rBC particles due to wildfire smoke. Code Availability The MATLAB code used to generate the CPMA-SP2 results is publicly available at https://github.com/tsipkens/bidias. This code is specifically designed to invert tandem aerosol measurments, particularly in this paper CPMA-SP2 measurements to find two-dimensional size distributions. For generating SP2-LEO and Lagtime analysis, the SP2 toolkit version 4.118 was used. This toolkit is also publicly accessible and can be found at https://zenodo.org/records/3575186. Schwarz, J.P., & Gao, R.S. (2019).SP2 toolkit 4.118. Zenodo repository. Available at https://zenodo.org/records/3575186. Last accessed 2024-05-27.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.011 |
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".