Modeling and interpretation of stable carbon isotope ratios of ethane in global chemical transport models
Bibliographic record
Abstract
Model calculations with two global 3D‐CTMs (GISS and MOZART‐2) in which we introduced ethane stable carbon isotopic ratios were performed. In both models, emission inventories based on the EDGAR database are used for VOC emissions. We considered source specific isotope fractionations and included global emissions from C3 and C4 plants which differ significantly in isotope ratio. Comparison of the model results with observation strongly indicates that the EDGAR emission inventory underestimates global ethane emissions by a factor of approximately 1.5. On the basis of the latitude‐dependent differences between model predictions and the atmospheric observations of ethane reported by Rudolph (1995), estimates of magnitude and latitude range of sources missing in current emission inventories are made. However, the concentration data alone provide only limited constraints on the geographical distribution and only indirect information about the type of the missing sources. Isotope ratio studies can be very valuable to obtain additional insight. To study the dependence between the geographical distribution of the emissions and atmospheric ethane concentrations and isotope ratios, MOZART‐2 model calculations were made where all emissions are concentrated in latitude bands as well as in specified regions. Two regimes can be distinguished on a global scale: In the source latitude band, dilution with background air explains most of the calculated concentration variation, while at latitudes farther away from the sources, chemical loss is the dominating process.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".