Nitrous oxide prediction through machine learning and field-based experimentation: A novel strategy for data-driven insights
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Applying machine learning to predict complex environmental phenomena like greenhouse gas emissions (GHG) is gaining significant attention. This study introduces innovative ensemble learning models that integrate the randomizable filter classifier (RFC), regression by discretization (RBD), and attribute-selected classifier (ASC) with the random forest (RF) algorithm, resulting in hybrid models (RFC-RF, RBD-RF, and ASC-RF). These models predicted nitrous oxide (N 2 O) and water vapor (H 2 O) emissions from agricultural soils. These model were benchmarked against a support vector regression (SVR) model. The dataset comprised 401 samples from potato fields in Prince Edward Island (PEI) and 122 samples from New Brunswick (NB), including measurements of N 2 O and H 2 O and related input variables such as soil moisture (SM), temperature ST, electrical conductivity (EC), wind speed, solar radiation, relative humidity, precipitation, air temperature (AT), dew point, vapor pressure deficit, and reference evapotranspiration. Feature selection and optimization of input scenarios were achieved using a combination of particle swarm optimization (PSO) and manual methods. Model performance was evaluated using multiple metrics: scatter plots, kite diagrams, density distribution histograms of relative percentage error, coefficient of determination (R 2 ), Nash–Sutcliffe efficiency coefficient (NSE), Percent of BIAS (PBIAS), coefficient of uncertainty at the 95% confidence level (U95%), Kling–Gupta efficiency (KGE), Willmott index of agreement (WI), and Legates and McCabe coefficient of efficiency (LME). Results demonstrated that the hybrid RFC-RF model outperformed the other models for N 2 O and H 2 O predictions in PEI and NB, followed by the RBD-RF, ASC-RF, and SVR models. The new models demonstrated good performance according to R 2 values, while the SVR model ranged from unacceptable to good. The study found that combining soil and climatic variables improved prediction accuracy, with ST, AT, and soil EC being the most influential variables. SHapley Additive exPlanations (SHAP) analysis confirmed the importance of ST for both N 2 O and H 2 O predictions. The findings underscore the significance of dataset length over input-output correlation and indicate that combining soil and climatic variables enhances model prediction accuracy. The developed models offer reliable and cost-effective tools for researchers, policymakers, and stakeholders to effectively predict and manage GHG in agricultural contexts.
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.
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 | 0.000 |
| 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) | 0.000 | 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 it