Assessment of soil CO2 and NO fluxes in a semi-arid region using machine learning approaches
Bibliographic record
Abstract
Agricultural lands are sources and sinks of greenhouse gases (GHGs). The identification of the main drivers affecting GHGs is crucial for planning sustainable agronomic practices and mitigating global warming potential. The main aim of this research was to evaluate the impact of environmental drivers (soil temperature and water-filled pore space, WFPS) and crop residue rates on CO2, NO, and NOx fluxes under conventional tillage (CT) and no-tillage (NT) systems. The accuracy of Random Forest Regression (RFR), Multiple Adaptive Regression Splines (MARS), and General Linear Models (GLM) in predicting CO2, NO, and NOx fluxes were also assessed. In both CT and NT systems, CO2, NO, and NOx fluxes decreased with increasing WFPS. Increasing temperature resulted in higher CO2 emissions and lower NO and NOx emissions. Higher residue rates resulted in significant increases in CO2 emission, whereas the NO and NOx emissions increased by decreasing the ratio of residue. For CO2 prediction, the RFR provided the largest R2 with the observed data. For NO–N and NOx-N prediction, RFR was the most efficient algorithm, but NO–N can be predicted with better accuracy. The output of this research highlights the importance of agronomic practices for climate mitigation, along with the possibility of using RFR to predict GHGs fluxes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".