Advances in engineered remediation for use in the Arctic and Antarctica
Bibliographic record
Abstract
Creative remediation schemes have been implemented with success at petroleum-contaminated sites in Alaska and Canada during the past decade. Contaminated media have been landfarmed, amended with fertilizers, augmented with microbial products, and manipulated with engineered systems. Phytoremediation developments and use of biodegradable synthetic and polymeric resins for potential use with petroleum and xenobiotic contaminants are on the horizon. Treatment of supra-permafrost water and melt-water runoff with permeable reactive barriers and partitioning bioreactors is now possible. Cost and time limitations will likely continue to drive remediation decisions in the Arctic. Environmental policy, environmental constraints, and cost will dictate what technologies are appropriate for Antarctic clean-up, although the pressure of time is less acute because land transfer and liability are not drivers. This paper discusses some recent advances in remediation engineering for use in polar regions. Conceptual models are presented, and case study treatment costs and durations are highlighted to aid environmental decision-making.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".