Geophysical Quantification of Water Percolation Quotient in an Alluvial Agricultural Soil
Bibliographic record
Abstract
Efficient water use planning is crucial for the sustainability of irrigated agriculture in California, where alluvial geological materials with indigenous salts impinge on crop growth. To facilitate irrigation scheduling and cultivation planning, it is necessary to determine water percolation quotients (WPQ) required for removal of excess salts from the rhizosphere. In order to estimate real-time WPQ, we conducted electromagnetic geophysical surveys at a saline farmland followed by stochastic computations. Results showed a wide variability in salinity that reached 16 dS m-1 in some locations. About 95% of the surveyed samples surpassed 2 dS m-1. Despite spatially dependent asymmetric variability and skewness (-0.13 to 1.90), the WPQ distribution patterns were consistently quantified with low errors (< 0.06). The sensor responses in the fields reached 100% cumulative frequency at a threshold of 13.6 dS m-1. Up to 49% of WPQ data ranged from 0.1 to 0.2. The WPQ decreased with increasing salinity and the zones with low quotient values represented areas where plant growth could be impaired. High WPQ levels demarcated zones with potential solute dissolution and dispersion. Overall, evaluation of WPQ can benefit irrigation planning and crop management practices while enhancing water use efficiency for agricultural production in farms that have been affected by drought and water shortage, and crop growth can be sustained at WPQ level that maintains salts below the crop tolerance threshold.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".