The appropriate geochemical monitoring of toe seepage from a mine-rock dump
Bibliographic record
Abstract
Water passing through mine-rock dumps either enters the underlying groundwater system or exits at the toe. For the case of toe seepage, the water is often collected in ditches and diverted out of the area. In light of the variability in flow and chemistry expected in toe seepage, questions arise as to the appropriate monitoring program including, for example, the importance of variable sampling frequency. Alternatively, for many mines in British Columbia, the more important question is "Are we missing anything important by sampling on a routine basis such as monthly?". Rather than answering the question based on theory, this paper answers with actual data from a minesite in British Columbia. At the Island Copper Minesite on Vancouver Island, British Columbia, an ambitious monitoring program of toe seepage took place over a six-month period. Eight stations were monitored basically either (1) once daily for flow and chemistry or (2) hourly for flow and every four hours for chemistry. Based on statistical analyses of data from selected stations, answers are provided for important technical questions, such as monitoring frequency, and for regulatory questions, such as permit limits. In essence, water chemistry can be viewed like hydrology where, for example, yearly concentrations of a 1-hour duration can be determined. This concept is expanded further in an accompanying paper at this symposium using standard monitoring data.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".