Assessing spatial distribution and joint uncertainty of TPH-fractions: Indicator kriging and sequential indicator simulation
Bibliographic record
Abstract
Petroleum hydrocarbon (PHC) contamination is one of the most common contaminants in soils and remediation of PHC-contaminated sites requires methods for characterizing the spatial distribution of PHC on a site. Few studies have compared the performance of indicator kriging (IK) and sequential indicator simulation (SIS) in site characterization of petroleum-contaminated sites, or the application of these methods given the fraction based guidelines. The objectives of this study were to determine if IK and SIS indicate similar contaminated areas and to examine how the probability of exceeding thresholds changes when multiple fractions are considered simultaneously. An abandoned refinery near Kamsack, Saskatchewan, characterized by clay-textured soils was sampled and analyzed for PHC fractions (F2 and F3). The probability of a location exceeding a fraction’s remediation criteria was determined using IK and SIS. Based on critical probability thresholds, IK indicated a greater area was contaminated by F2 (6.3%) and F3 (0.8%) than SIS (4.5 and 0.6%, respectively). When the remediation criteria for both F2 and F3 were considered simultaneously, “dependent” and “independent” cases were examined. The dependent case assumed perfect correlation and used the maximum probability of either F2 or F3 as the new estimate. The independent case assumed no correlation and evaluated the probability of F2 > 2500 mg kg–1 or F3 > 6600 mg kg–1. The dependent case resulted in a smaller contaminated area than the independent case in both IK and SIS. On this site the differences between the two methods were small, although IK did smooth the distribution. Key words: Sequential indicator simulation, indicator kriging, geostatics, petroleum hydrocarbon contamination, uncertainty
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".