Spatial Analysis of Salt Heterogeneity in a Central Californian Grape Field
Bibliographic record
Abstract
Grape is one of the major crops produced in California; however, moderate salt concentrations in the soil pore space can affect its growth. Therefore, it is important that the root zone salinity be rigorously monitored and maintained below the threshold limit for grape. Spatial analysis of salt heterogeneity can facilitate site-specific management for the crop. We conducted an electromagnetic salinity survey followed by semivariance modeling and kriging analyses in order to quantify the spatial variability and structure of salt distributions in a central Californian grape field. Soils in the study area had silty clayey texture and the salinity across the root zone remained largely elevated with random variations. Around 78 and 19 % of the salinity values remained in the 3-4 and 4-4.5 dS m-1 ranges, respectively. The 3.5-4 dS m-1 level represented one of the most abundant concentrations across the areas of salt build up in the grape field. Based on our variogram and crossvalidation analyses, the Gaussian model fitted best to the salinity data (r2 = 0.996, RSS = 0.0003). The salt heterogeneity was characterized by high structural variance (99 %) and low nugget effect (C0 = 0.014). The kriging analysis demonstrated a large spatial dependence of salinity that extended beyond a 1000 m radius. The isotropic nature of the spatial correlation structure produced an omnidirectional variogram. The overall salinity levels across the field surpassed the salt tolerance threshold for grape and the spatial analyses identified sensitive zones and their structures that require precise adaptive management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".