Bibliographic record
Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> Estimating regional CO<sub>2</sub> sources and sinks is challenging due to limited data and uncertainties in transport models. Orbiting Carbon Observatory-2 (OCO-2) overcomes measurement limits, providing CO<sub>2</sub> variations beyond in-situ networks. This study analyses altitude-wise model-observation CO<sub>2</sub> differences from surface to upper troposphere using aircraft observations from ATom, Amazon, and CONTRAIL campaigns over OCO-2 total column CO<sub>2</sub> (XCO<sub>2</sub>) 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 XCO<sub>2</sub> (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 CO<sub>2 </sub>flux errors. In contrast, ACTM better matches aircraft CO<sub>2</sub> in the middle (2–5 km) and upper (5–8 km) troposphere, likely due to accurate large-scale transport representation. Over the Amazon, CO<sub>2</sub> 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 CO<sub>2</sub> 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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".