In-situ groundwater treatment using ARUM: IRAP/NRC final report 2000
Bibliographic record
Abstract
In 1995, after 10 years of monitoring the ground water flow from a small Cu/Zn base metal \ntailings depositinNorthernOntario,the ground water flow paths were re-evaluated to confirm \npreviously predicted flowdirections. A highly contaminated ground water seepage had taken \nin 1996 a different route and was emerging to the surface contaminating a small lake (Mud \nLake). However the seepage path was well defined hydro-geologically and hence it may be \nsuitable for in-situ-treatment. Geo-microbiological in-situ treatment approaches were \nconsidered jointly with Dr. Ferris ( University of Toronto). It was proposed, that through \nmicrobial urea degradation ground water pH could be increased, resulting in metal \nprecipitation in-situ improving the seepage discharge quality. \nA research program was initiated in 1997/98 based on the concept ofin situ-increasing the \npH through microbial activity which should result in metal precipitation (Schematic 1). This \napproach needed to be substantiated with microbiological testing and geochemical \nmodelling. This was carried out by Dr. G. D. Ferris at the University of Toronto. Boojum \nResearch Ltd. developed a ground water model for the site to define the quantity of \ngroundwater to be treated and field tested urea degradation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".