Multifidelity-based Gaussian process for quasi-site-specific probabilistic prediction of soil properties
Bibliographic record
Abstract
Conventional empirical equations for soil properties prediction tend to be site-specific, exhibiting poor reliability and accuracy. Meanwhile, alternative data-driven methods require large datasets for training. To address these issues, this study proposed a novel multifidelity residual neural-network-based Gaussian process (MR-NNGP) modelling framework. A soil property low-fidelity (LF) prediction model is first trained using abundant LF data collected from worldwide sites for generating preliminary estimation. A high-fidelity (HF) model is subsequently trained on sparse HF data from the specific site of interest for calibrating the LF model to make quasi-site-specific predictions. An infinitely wide NN-inspired NNGP is adopted as the baseline algorithm for training LF and HF models. The compression index of clays is selected as an example to examine the capability of the proposed MR-NNGP. The results indicate that the compression index of clays can be well captured by MR-NNGP, exhibiting superior accuracy and reliability compared with one-shot training without using MR modelling and other baseline algorithms such as GP. The MR-NNGP framework alleviates data dependency and improves model performance through hierarchical modelling on relatively simple correlations using a superior algorithm. Unified LF data and efficient hyper-parameter optimization indicate the flexibility for broader applications in various sites worldwide.
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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 | 0.000 | 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 | 0.001 |
| 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".