Bibliographic record
Abstract
Nitrous oxide (N2O) emissions from croplands are one of the most important greenhouse gas sources, and it is difficult to simulate on a large scale. In order to simulate N2O emissions from global croplands, a new version of the process-based TRIPLEX-GHG model was developed by coupling the major agricultural activities. The coefficient of the NO3− consumption rate for denitrification (COEdNO3) was found to be the most sensitive parameter based on sensitivity analysis, and it was calibrated using field data from 39 observation sites across major croplands globally. The model performed well when simulating the magnitude of the daily N2O emissions and was able to capture the temporal patterns of the N2O emissions. The COEdNO3 ranged from 0.01 to 0.05, and the continental mean of the parameter was used for the model validation. The validation results indicate that the means of the measured daily N2O fluxes during the experiment periods are highly correlated with the modeled results (R2 = 0.87). Consequently, our model simulation results demonstrate that the new version of the TRIPLEX-GHG model can reliably simulate N2O emissions from various croplands at the global scale.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.034 | 0.016 |
| Insufficient payload (model declined to judge) | 0.042 | 0.054 |
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".