Comment on egusphere-2024-2584
Bibliographic record
Abstract
Abstract. Extensive industrial operations in the Athabasca Oil Sands Region (AOSR), Alberta, Canada, are a suspected source of water-soluble brown carbon (WS-BrC), a class of light-absorbing organic aerosols capable of altering atmospheric reaction chemistry and planetary albedo. However, the current understanding of WS-BrC throughout the AOSR is limited, and the primary regional sources of these aerosols are unknown. During the summer of 2021, active filter-pack samplers were deployed at five sites throughout the AOSR to collect total suspended particulate for the purpose of evaluating WS-BrC. Ultraviolet – visible spectroscopy and fluorescence excitation-emission matrix (EEM) spectroscopy, complimented by Parallel Factor Analysis (PARAFAC) modelling, were employed for sample characterization. Combined EEM-PARAFAC analysis identified three fluorescent components (fluorophores), including one humic-like (C1) and two protein-like (C2, C3) substances. Sites near oil sands facilities and sample exposures receiving atmospheric transport from local industry (indicated via back-trajectory analysis) displayed increased C1 and C3 fluorescence; moreover, both fluorophores were positively correlated with particulate elements (i.e., vanadium, sulphur) and gaseous pollutants (i.e., nitrogen dioxide, total reduced sulphur) indicative of oil sands emissions. Fluorophore C2 presented high emission intensity at near-field sites and during severe wildfire smoke events, while positive correlations with industry indicator variables suggested C2 was likely reflective of both wildfire-generated and anthropogenic WS-BrC. These results demonstrated that combined EEM-PARAFAC analysis is an accessible and cost-effective tool that can be applied to monitor industrial WS-BrC in the ASOR.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.018 | 0.008 |
| Insufficient payload (model declined to judge) | 0.393 | 0.276 |
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".