Prediction of soil-water characteristic curves using two artificial intelligence (AI) models and AI aid design method for sands
Bibliographic record
Abstract
In this paper, two artificial intelligence (AI) models (i.e., artificial neural networks (ANN) and multivariate adaptive regression splines (MARS)) were developed using cumulative percentiles from the grain-size distribution (GSD) curve as input information to predict the soil-water characteristic curve (SWCC). The importance of each input variable was testified using two different sensitivity analyses. The results show a strong correlation between the SWCC and GSD curves based on large volume of datasets. The ANN provides higher accuracy due to its unique structure; however, the MARS model facilitates in developing a regression equation that contributes to stable performance. The SWCC can be reliably predicted with the MARS regression equation using one data point from the GSD curve and bulk density information. Sensitivity analysis suggests that the prediction of the SWCC is also possible with a reasonable degree of accuracy by using single data point information from the GSD curve as an input variable. Finally, a novel AI aid design method is proposed by combining the MARS regression equation along with physico-empirical model and fitting equation that provides a rapid and reliable technique for predicting the SWCC of sands.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".