Movement of petroleum through freezing and frozen soils
Bibliographic record
Abstract
Introduction Movement of petroleum through non-freezing soils has been studied extensively over the last several decades. Little work has been done on understanding how petroleum moves through seasonal freezing soils (active layer) and frozen soil (permafrost). Petroleum migration through active layer and permafrost soils is influenced by the formation and presence of ice at all scales. At the millimeter scale, ice in pore spaces will either interrupt downward migration causing petroleum to spread laterally, or impede petroleum movement altogether due to the lack of open pore space. Segregated ice at centimeter-to-meter scales will most likely cause the contamination to spread laterally in frozen soils. Segregated ice formation in the active layer can also generate fissures that will enhance petroleum movement when the soil is thawed. At larger scales, discontinuous and continuous permafrost will slow, redirect, or impede contaminant migration. Understanding the impact freezing and frozen soil conditions have on petroleum movement through soils is necessary to regulation, assessment, and cleanup of contaminated soil and groundwater. A good example of this impact is provided when considering natural attenuation. Seasonal ice and post-cryogenic structure present in active layer soil will influence the movement of petroleum and dissolved compounds, thereby impacting the design of monitoring systems to track natural attenuation. Moreover, cold soil temperatures will slow the physical weathering of compounds in the subsurface. Cleanup levels established for cold regions contaminated soil (Chapter 1) and any remediation plan developed for these sites must account for these impacts.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".