A DECISION TREE-BASED APPROACH TO CALCULATE NITROUS OXIDE FLUXES FROM CHAMBER MEASUREMENTS
Bibliographic record
Abstract
Soil nitrous oxide (N2O) fluxes are commonly measured with nonsteady state chambers using slope values derived from linear or quadratic regression, fitted to the change in N2O concentration over time (dC/dt); however, these methods frequently underestimate N2O flux values. Here, we propose a decision tree-based model (DTBM) to better match curve shape with linear and nonlinear models to estimate dC/dt. The DTBM was compared with linear, quadratic regression, and the Hutchinson–Mosier (H–M) equation. The objectives were to (i) evaluate curve shape classification; (ii) evaluate dC/dt response to uncertainty, and (iii) determine method effect on cumulative N2O emissions and emission factor. Curve shapes with increasing N2O concentration over time had the highest proportion of data (52%–55%). Mean N2O flux calculated with DTBM showed to be less responsive to data variability, and therefore, more stable than the other methods. Data classification included in DTBM offered an improved method for calculating cumulative N2O emissions in low-flux situations, whereas under a high-flux situation, all methods tested were acceptable to calculate N2O emissions. The DTBM proved to be a robust method of matching each data type with the best model for calculating an individual flux and to accurately calculate cumulative N2O emissions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".