ENV-638: ADVANCED TECHNIQUES FOR SITE CHARACTERIZATION: REAL-TIME HIGH-RESOLUTION SITE CHARACTERIZATION OF THE SUBSURFACE USING MIP, LIF AND HPT
Bibliographic record
Abstract
In-situ site characterization can be a key component of site delineation and remediation. In-situ site characterization allows for large amounts of detailed data to be collected quickly and cost-effectively compared to traditional techniques. These data, combined with traditional Phase II Environmental Site Assessment (ESA) methods, greatly enhance the understanding of the presence, concentration and distribution of contaminants in the subsurface, which can lead to more efficient and focused monitoring and remediation programs.\nThree high-resolution characterization technologies have recently been introduced to Canada. These include the Membrane Interface Probe (MIP) for dissolved-phase contamination, the Laser-Induced Fluorescence (LIF) probe for free-phase petroleum hydrocarbon (PHC) contamination, and the Hydraulic Profiling Tool (HPT) for measuring the subsurface permeability and ultimately estimating the hydraulic conductivity of the subsurface. All three probes are advanced to depth by direct push methods (Geoprobe System™). The LIF probe consists of a fiber optic cable that emits an ultraviolet light through a window during probe advancement. The PAHs in PHCs fluoresce and the response is measured by instrumentation at the surface in real-time. The MIP is a heated probe that is used to volatilize dissolved and sorbed contaminants. The contaminants diffuse through a semi-permeable membrane on the probe and are subsequently transported to the surface for analysis. The HPT probe injects a constant flow of clean water from surface and utilizes a downhole pressure transducer to measure subsurface permeability above and below the water table. Hydraulic conductivity can be estimated in the saturated zone at the conclusion of each push.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".