NO2 vertical column density retrieval from Pandora and first comparison with GEMS data during the GMAP & SIJAQ campaign 2020
Bibliographic record
Abstract
We, for the first time, compared NO2 VCDs obtained from Ground-based Pandora spectrometer which uses direct sun measurements and those obtained from GEMS which is the first UV-VIS hyperspectral sensor onboard the geostationary earth orbit satellite during the GMAP (GEMS Map of the Air pollution) & SIJAQ (Satellite Integrated Joint monitoring of Air Quality) campaign 2020 held in winter. NO2 VCDs were retrieved using DOAS technic from four Pandora spectrometers which were located over four Seosan sites. During the inter-comparison period four Pandora spectrometers, we found 0.99 of correlation coefficient (R) between the four Pandora spectrometers. While slope and intercepts range from 0.90 to 0.99 and from ??? 5.15 1014 to 5.58 1014, respectively. The diurnal patterns show good agreement between NO2 VCDs measured by the Pandora and those measured by GEMS, TROPOMI (TROPOspheric Monitoring Instrument), and OMPS (Ozone Mapping and Profiler Suite). The R between NO2 VCDs obtained from the Pandora spectrometer and NO2 VCDs obtained from the GEMS range from 0.60 to 0.75. The comparison was also carried out accounting for horizontal representativeness of Pandora. This present study discuses a difference of comparison results obtained between Pandora and GEMS in cases with and without application of horizontal representativeness.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".