Bibliographic record
Abstract
Abstract. Estimating regional CO2 sources and sinks is challenging due to limited data and uncertainties in transport models. Orbiting Carbon Observatory-2 (OCO-2) overcomes measurement limits, providing CO2 variations beyond in-situ networks. This study analyses altitude-wise model-observation CO2 differences from surface to upper troposphere using aircraft observations from ATom, Amazon, and CONTRAIL campaigns over OCO-2 total column CO2 (XCO2) sampling location to characterise sources of uncertainty in MIROC4-ACTM. We show model aligns better with ATom tropospheric columns (0.03 ± 0.03 ppm) than OCO-2 XCO2 (0.2 ± 0.5 ppm), especially over oceans, highlighting the need for expanded profile measurements to characterise errors robustly. Altitude-wise comparisons reveal this differences primarily occur in the lower troposphere (0–2 km), likely due to ACTM's near-surface land CO2 flux errors. In contrast, ACTM better matches aircraft CO2 in the middle (2–5 km) and upper (5–8 km) troposphere, likely due to accurate large-scale transport representation. Over the Amazon, CO2 differences with aircraft and OCO-2 differ, likely due to a lack of regional surface sites for inversion and insufficient high-altitude profile (~4 km) not representative of XCO2. Over Asian megacity airports, which are significant emission hotspots, the model shows a large negative difference with CONTRAIL than OCO-2. This discrepancy likely hints that MIROC4-ACTM is unable to capture urban fossil CO2 emission signals at airports due to coarse resolution (~2.8° x 2.8°) and higher resolution of OCO-2 limits ability to fully capture actual emission footprints.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.021 | 0.016 |
| Insufficient payload (model declined to judge) | 0.308 | 0.204 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".