Antioxidant depletion in high-density polyethylene (HDPE) geomembrane with hindered amine light stabilizers (HALS) in low-pH heap leach environment
Bibliographic record
Abstract
Antioxidant depletion from a high-density polyethylene (HDPE) geomembrane with hindered amine light stabilizers (HALS) immersed in seven different low pH solutions is examined over a 3 year period. The examined solutions had the range of pH (0.5, 1.25, and 2.0) likely to encompass the pH of the leach solutions found in copper, nickel, and uranium heap leach pads. The metal concentration for these solutions is adopted from copper raffinate solutions. Additional solutions are investigated to examine the effects of field practices such as using surfactants in the leach solutions and pre-curing of the ores used to improve the metallurgical response of the ore. For the antioxidants detected by standard oxidative induction time (Std-OIT), there was a depletion to residual value of about 20% of the initial Std-OIT that varied depending on the incubation temperature and pH of the solution whereas decreasing the pH from 2 to 0.5 did not significantly affect the depletion rates of Std-OIT. The antioxidants detected by high-pressure oxidative induction time (HP-OIT) exhibited the fastest depletion in pH = 1.25 with the highest residual values followed by pH 2.0 and the slowest HP-OIT depletion was in pH = 0.5, but with the lowest residual values. Arrhenius modelling is used to predict the length of the antioxidant depletion stage for each solution based on both Std-OIT and HP-OIT.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".