Estimation of soil organic matter in mollisols based on artificial intelligence
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
Mollisols are a valuable natural resource, and their organic matter content can be used to evaluate soil fertility. Estimating and monitoring the soil organic matter (SOM) content of Mollisols is of great importance. This study employed an artificial intelligence method, based on deep neural networks (DNNs), to predict SOM content. In this method, relevant measurement values of soil nutrients, such as phosphorus, nitrogen and potassium, and the soil pH were used as input features for the model. A dataset comprising 2490 samples was used for model training and testing. These samples were obtained through soil sampling and experimental measurements. This study validated the model by setting different ratios of training and testing datasets, and the results indicated that the proposed method can estimate the SOM content with an accuracy of nearly 95%. Furthermore, the method developed in this study was compared with six traditional machine learning methods and exhibited higher accuracy. This model will serve as the basis for designing realtime non-destructive testing of SOM. • This study utilises AI model to accurately predict the soil organic matter content in Mollisols. • Soil nutrient measurements including phosphorus, nitrogen, potassium, and soil pH as input features of the AI model. • The research employed a dataset of 2490 samples by experimental measurements. • Comparison with seven traditional machine learning methods revealed that the DNNs-based model outperforms others in accuracy.
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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) | 0.002 | 0.001 |
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