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Record W1519382879 · doi:10.2166/wst.2000.0280

Bioleaching of copper mining residues by Aspergillus niger

2000· article· en· W1519382879 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueWater Science & Technology · 2000
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsUniversité LavalConcordia University
FundersNatural Resources CanadaMcGill University
KeywordsBioleachingAspergillus nigerResidue (chemistry)CopperChemistryLeaching (pedology)SucrosePulp and paper industryFood scienceWaste managementEnvironmental scienceBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

A study was initiated to determine the possibility of using the fungus Aspergillus niger for bioleaching and then to identify and evaluate the parameters that affect this process. An oxidized mining residue containing mainly copper (7240 mg/kg residue) was studied. Sucrose and mineral salts medium were initially used to produce citric and gluconic acids by A. niger with various concentrations of residue (1, 5, 7, 10 and 15% w/v). Maximal removal of up to 60% of the copper was obtained for the 5% residue. These experiments showed that the pH decreased to around three within 10 days of incubation. Other substrates were evaluated including molasses, corn cobs and brewery waste. Sucrose gave the best results for copper removal, followed by molasses, corn cobs and brewery waste. Other experiments using ultrasound as a pre-treatment showed that 80% removal of the copper could be obtained for a 5% residue concentration. In conclusion, leaching of copper from a mining residue is technically feasible using A. niger. Further research must be performed to increase the economic feasibility of the process.

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 score0.061
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.217
Teacher spread0.210 · 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