Oxygen consumption test to evaluate the diffusive flux into reactive tailings: interpretation and numerical assessment
Bibliographic record
Abstract
The oxygen consumption (OC) test is conducted on sulphide tailings by measuring the decline of oxygen concentration in a closed headspace, at the top of a cylinder, as a result of diffusion and oxidation reactions. For a short-duration test, the measurements may be interpreted using a simplified analytical method based on modified Fick’s laws, which provides the combined value of the effective oxygen diffusion (De) and reaction rate (Kr) coefficients of the tailings. This lump value can be used to evaluate the steady-state oxygen flux entering the exposed sulphide tailings. In this paper, a numerical parametric study is performed to investigate the effect of test duration and headspace height, h, on results obtained from OC tests. The assessment also considers tailings with different values of De and Kr. The results indicate that the simplified interpretation method usually tends to underestimate the surface oxygen flux, in proportions that depend on the testing conditions. Results from this study can be used to estimate the relative precision of the oxygen flux for specific conditions, thus helping practitioners decide how to best interpret testing measurements for a given application.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".