Electrical Conductivity of a Failed Septic System Soil Absorption Field
Bibliographic record
Abstract
Locating existing septic systems and determining the extent of soil contamination after septic system failure can be destructive, time consuming, and a nuisance to homeowners. The objective of this study was to determine the effectiveness of noninvasive electromagnetic induction (EMI) for locating a failed septic system in fine‐textured glacial‐till‐derived soils. Components of a failed septic system were located with a push probe, georeferenced with a theodolite, and surveyed with a dual receiver EMI sensor (DUALEM‐2) in December 2001 (wet soil moisture condition) and July 2002 (dry soil moisture condition). Three transects located perpendicular to the soil absorption field trenches were sampled to a depth of 1.2 m and used to ground reference the EMI survey. Near‐surface (1‐m depth) apparent conductivity (EC a ) was significantly correlated to unweighted average electrical conductivity from soil saturated paste extracts (EC sat ; r = 0.79). The EC a below the soil absorption field was higher than the surrounding native soil under both dry and wet soil moisture conditions. Individual soil absorption trenches had a higher EC a than background EC a under both soil moisture conditions. A higher EC a pattern that was apparent in December 2001 associated with discharge of wastewater at shallow depths was not evident in July 2002 after the system had been abandoned for 6 mo. While more research is warranted, results from this study suggest that electromagnetic induction is a promising technique to identify the location of septic system components, failed septic systems, and their associated effluent plumes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".