Teaching ARD reclamation techniques in high school
Bibliographic record
Abstract
It is well known that mining is not well perceived by the dominant North America society. The general public typically has a poor understanding of the entire mine cycle and are often influenced by negative news, especially those related to environmental issues. Initiative to rectify this problem should start by showing successful examples of environmentally friendly and socially responsible mining operations. In particular, the mining industry has endeavored to introduce to the general public, and in particular children, the basic ideals of the modern mining industry, which is revolutionizing both its practices and its image through the implementation of best mining practices. A proven technique for raising awareness of mining issues with children is that of hands-on education; hands-on methods are those that involve the student in the problem and attract their interest. The objective of this paper is to illustrate how land reclamation by the mining industry can be taught in the secondary school classroom. A kit was developed to show high school students how the mining industry is reclaiming the land and addressing acid mine drainage problems. An experiment on acid mine drainage, treatment and reclamation was devised to be taught to Earth Science 11 Geological Science (resources and environment) students. The topic to be covered relates to identification of environmental problems related to the development of a natural resource. The students take a sample of pyrite-rich mine waste or crushed pyrite and perform an experiment to see if it produces an acidic solution under certain variable conditions. Either a bicycle pump to add air into the solution or hydrogen peroxide provides the chemical conditions for pyrite oxidation. Bacteria (Thiobacillus ferrooxidans) is added to another vial with pyrite to increase the oxidation reactivity and to compare with the chemical oxidation procedure. After obtaining an acidic pH, the students raise the solution's pH to a neutral condition using lime. A flocculent is then added to precipitate the metal ions from solution and to create metal-hydroxide sludge; this also aids in the subsequent dewatering stage. The sludge is then used to grow marigolds when mixed with peat moss. It has been found that the marigolds grow easily in the sludge/peat moss soil and in some cases have performed better than the control potting soil. The various phases of the experiment development including student responses are documented in this paper.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.016 |
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".