An innovative approach for visualization of subsurface soil properties
Bibliographic record
Abstract
Subsurface characterization is an important requirement in the decision-making process of selecting a remediation technique for petroleum-contaminated sites. The soil type distribution is one of the most important site characteristics, because it affects selection of the site remediation technique. The visualization of soil type distribution and also the contaminant concentration distribution in the subsurface can help the decision-maker understand the site and select the proper remediation technique. In this paper, we describe the software Soil-Visual (1.0, 1.1), which is used for visualizing the soil sampling data, the soil type distribution, and contaminant concentration distribution of a contaminated site. This software has two functions: (1) to determine the soil particle size distribution and contaminant concentration distribution of the entire site from limited soil sampling data; and (2) to visualize the multi-dimensional soil type distribution and contaminant concentration distribution data of each soil layer on a two-dimensional map. The red-green-blue (RGB) color illustration method has been used in this software to convert the multi-dimensional soil sampling data into a bitmap. Key words: RGB bitmap, soil classification, visualization
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".