Bibliographic record
Abstract
The increase of atmospheric CO2 concentration has significant climate impacts, with many countries worldwide (including Canada, the United States, China, and members of the European Union) having set a net-zero emission goal for the following decades, which makes accurate measurements of its spatial and temporal variability crucial. One of the outstanding challenges is to observe the vertical distribution and variation of CO2. Although the mean column CO2 is useful for many climate applications, CO2 is known to vary vertically depending on the season and time of the day, so reflecting this behavior would help reduce biases in column CO2 products due to the vertical distribution uncertainty. Having this information would also assist in identifying emission sources (e.g., local compared to emissions from another city) and atmospheric processes controlling the atmospheric CO2 distribution. This study examines the potential for measuring CO2 vertical distribution and implementing innovative methods to perform profiling measurements of CO2 using a ground-based remote sensing infrared instrument, the Atmospheric Emitted Radiance Interferometer (AERI). To verify the feasibility of CO2 vertical profile retrieval, a simulation experiment-based assessment was conducted which replicates different instrument settings, using a Line-By-Line Radiative Transfer Model (LBLRTM) as the forward model and the Optimal Estimation as the inverse method. By evaluating key metrics of the retrieval technique, such as the Degrees of Freedom for Signal (DFS), it was verified that vertical profiling of CO2 using AERI is possible given the expected CO2 variability at city level and the noise level of the AERI instrument. It was also assessed that vertical levels closer to the surface are best sounded, with an accuracy of up to 5 ppmv on lower levels (from surface to around 800 m) assuming a 5% CO2 variability every 1 km height and actual AERI’s noise level. Lastly, the retrieval algorithm was applied to the real measurements of AERI acquired together with independent atmospheric sounding data in a field campaign in order to verify the CO2 sensing accuracies
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 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".