MétaCan
Menu
Back to cohort

Effectiveness of Covers and Liners Made of Red Mud Bauxite and/or Cement Kiln Dust for Limiting Acid Mine Drainage

2005· article· en· W2044530119 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Environmental Engineering · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicMine drainage and remediation techniques
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsLeachateBauxiteCement kilnAcid mine drainageRed mudCementTailingsEnvironmental scienceKilnWaste managementEnvironmental chemistryChemistryPulp and paper industryEnvironmental engineeringMetallurgyMaterials science

Abstract

fetched live from OpenAlex

This paper presents a laboratory investigation to evaluate the capacity of alkaline residues to inhibit acid mine drainage. Column tests were used to evaluate the geochemical behavior of cement kiln dust (CKD) and red mud bauxite (RMB) used as covers, liners, or mixed with acid producing tailings and waste rocks. The most important indicators of neutralization are pH and the concentrations of metals in solution. Initial leachate pH of samples with an alkaline cover composed of 10% CKD or 10% of a mixture of CKD and RMB was low, but rapidly increased to near 7.0 and stabilized for the duration of this study. The use of alkaline materials as a liner had a positive effect on the reduction of Fe, SO4 and other metals such as Cu and Zn concentrations and the number of viable bacteria. In the cases where the alkaline layer was used as a liner or mixed with the waste rocks, near neutral pH values were rapidly reached in the leachate. However, in these columns the leachate pH values decreased over time.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.005
GPT teacher head0.204
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it