MétaCan
Menu
Back to cohort
Record W2149302950 · doi:10.1002/jctb.830

Bioleaching of copper and other metals from low‐grade oxidized mining ores by <i>Aspergillus niger</i>

2003· article· en· W2149302950 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

VenueJournal of Chemical Technology & Biotechnology · 2003
Typearticle
Languageen
FieldEngineering
TopicMetal Extraction and Bioleaching
Canadian institutionsConcordia University
FundersConcordia University
KeywordsBioleachingAspergillus nigerCopperCopper oreLeaching (pedology)ChemistryMetallurgySulfuric acidResidue (chemistry)Pulp and paper industryEnvironmental chemistryEnvironmental scienceMaterials scienceFood science

Abstract

fetched live from OpenAlex

Abstract A study was initiated to determine the feasibility of using the fungus Aspergillus niger for bioleaching metals from oxide low‐grade ore. Large quantities of the metals are embodied in the low‐grade ores and mining residues that can be recovered. Presently available techniques (pyrometallurgical and hydrometallurgical) are expensive or may have a negative impact on the environment. An oxidized mining ore containing mainly copper (7245 mg kg −1 residue) was studied. In this study, the fungus A niger produced a variety of organic acids. Addition of small quantities of sulfuric acid enhanced the organic acids, efficiency. Various agricultural wastes were evaluated as substrates and a maximum solubilization of 68% for copper for a medium containing potato peels was achieved. In conclusion, leaching of copper from a mining ore is technically feasible using A niger . Further research must be performed to increase the rate of copper removal. Copyright © 2003 Society of Chemical Industry

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 score0.051
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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