APPLICATION OF ENSEMBLE LEARNING IN ADAPTIVE SURFACE MODELING OF SOIL TOTAL POTASSIUM CONTENT IN COMPLEX LANDFORM AREAS
Bibliographic record
Abstract
The spatial distribution of soil properties is affected by complex geological environmental factors, and the spatial differentiation characteristics are very obvious. It is difficult to achieve high-precision simulation using a single global interpolation model to simulate soil properties. For the characteristics of spatial discontinuity, limited accuracy of global interpolation models and poor adaptability, this paper proposes an adaptive surface modeling method of soil properties (ASM-SP) supported by ensemble learning and integrating geoscientific environmental variables. Using 110 sample point data collected in 2013, regression kriging (RK), Bayesian kriging (BK), ordinary kriging interpolation (OK), inverse distance weighting (IDW), ASM- SP, the total potassium content of soil was interpolated in Qinghai Lake complex landform type area. This article uses the point-by-point cross validation (LOOCV) interpolation method to simulate accuracy. The results show that ASM-SP not only takes into account the nonlinear relationship between geological environmental variables and soil properties, but also integrates the adaptability advantages of multiple models. It is a new method to achieve high-precision simulation of total soil potassium content in complex landform areas.
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.
How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".