Reactivity of materials from phosphate mines
Bibliographic record
Abstract
Both iron and phosphate, if they occur separately in the environment, can be the source of serious environmental problems. But since they have a strong chemical affinity for each other they normally tend to keep each other in check. Since iron is one of the major drivers of acid generation many studies have explored the possibility of mixing various phosphate products, such as commercially-available fertilizer or dissolved phosphate salts, with acid generating mine wastes to reduce or even eliminate acid mine drainage (Spotts and Dolhopf, 1992; Meek, 1991; Hart et al., 1990; Hart & Stiller, 1991; Evangelou, 1994; Ziemkiewicz, 1990; Yanful et al., 2000; Dey et al., 2000). In general, the studies have concluded that while phosphate deployed in such a manner will definitely inhibit AMD, but that material and application costs make it economically impractical. Since 1992 Boojum Research Limited has been conducting large scale field trials in which it has applied natural phosphate rock (NPR), a granular waste product of phosphate mines operated by Texas Gulf in North Carolina, to acid-generating mine wastes. These have demonstrated not only that phosphate works, but that it can provide an extremely economical solution to AMD; the trials have shown both that phosphate is effective in lower application rates as proposed by other workers, and also the phosphate is applied differently than previously suggested. It appears to be effective when it is merely scattered onto the target area - either onto tailings deposits or on waste rock, or on acid-impacted sediments of lakes - rather than mechanically mixed into the waste stream. Since the material cost of NPR is negligible the only major expenditure associated with its use, is shipping. Accordingly, laboratory tests conducted by Boojum to determine the specific chemical behaviour of the Texas Gulf North Carolina NPR, were enlarged to include NPR from mines in Ontario.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".