Total Dissolved Salt Prediction Using Neurocomputing Models: Case Study of Gypsum Soil Within Iraq Region
Bibliographic record
Abstract
Quantification of the soil physicochemical properties is one of the essential process in the field of soil geo-science. In the current research, three types of machine learning (ML) models including support vector machine (SVM), random forest (RF), and gradient boosted decision tree (GBDT) were developed for Total Dissolved Salt (TDS) prediction over several locations in Iraq region. Various physicochemical soil properties were used as predictors for the TDS prediction. Four modeling scenarios are constructed based on the types of the associated soil input variables properties. The applied ML models were analyzed and discussed based on several statistical measures and graphical presentations. Based on the correlation analysis; Gypsum concentration, Sulfur trioxide ( SO3), Chloride (Cl), and organic matter (OR) were the essential soil properties for the TDS concentration influence. The prediction results indicated that incorporating all the types of input variables including chemical, soil consistency limits, and soil sieve analysis attained the best prediction process. In quantitative terms, the SVM model attained the maximum coefficient of determination ( R2=0.849) and minimum root mean square error (RMSE=3.882). Overall, the development of the ML models for the TDS of soil prediction provided a robust and reliable methodology that contributes to the soil geoscience field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".