Prediction of Soil Organic Carbon Contents of Rice Paddies in South-Western Coastal Area of Korea Using Random Forest Models
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
Random forest models (RFM) are useful in predicting the soil carbon (C) contents because RFM predicts soil C with high accuracy under complicated environmental conditions. However, there are very few studies on prediction of soil C using RFM in Korea. Moreover, there is no case study using RFM to predict soil C content of reclaimed tideland (RTL) soils, which have high C sequestration capacity. Therefore, in this study, the applicability of RFM was evaluated using published soil properties data, including soil C and soil variables, for RTL soils located in southwestern coastal areas of Korea. In the present study, RFM was built using the data of 16 variables (e.g., sand, silt, and clay contents, pH, electrical conductivity of saturated soil paste (EC e ), and nutrient concentrations) obtained from five RTLs with similar climate, topography, and vegetation. The 80% of the total data were trained to build the model, and searched optimal hyper parameters were used to improve accuracy. The determination coefficient (R 2 ) of the model was 0.67, and the difference between measured and predicted soil C content was 25.9% on average. However, when the measured values were out of the range of the data trained for building the model or the measured values were close to the minimum or maximum value, the difference between the predicted and measured values became larger (73.9%). The contribution of the independent variables to the prediction of soil C using the model was the greatest (14.9%) for soil NH 4 + concentrations. Meanwhile, the contribution of EC e , which was highly correlated with soil C content, was not detected, suggesting that the importance of the number and range of training data used to build model. Our study shows the possible application of RFM to predict soil C contents of RTL soils in Korea, and further highlights that a large amount of data should be accumulated for high accuracy prediction of soil C using RFM.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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