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Record W2256502493 · doi:10.3390/min6010011

Mineralogy, Geochemistry and Stable Isotope Studies of the Dopolan Bauxite Deposit, Zagros Mountain, Iran

2016· article· en· W2256502493 on OpenAlexaff
Somayeh Salamab Ellahi, Batoul Taghipour, Alireza Zarasvandi, Michael I. Bird, Alireza K. Somarin

Bibliographic record

VenueMinerals · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsBrandon University
FundersShiraz University
KeywordsBauxiteGeologyGeochemistryIsotope geochemistryStable isotope ratioIsotopeMineralogyChemistry

Abstract

fetched live from OpenAlex

A new heterotrophic strain, named Providencia sp. JAT-1, was isolated and used in bioleaching of low-grade complex copper ore. The strain uses sodium citrate as a carbon source and urea as a nitrogen source to produce ammonia. The optimal growth condition of the strain is 30 C, initial pH 8, sodium citrate 10 g/L and urea 20 g/L, under which the cell density and ammonia concentration in the medium reached a maximum of 4.83 × 108 cells/mL and 14 g/L, respectively. Ammonia produced by the strain is used as the main lixiviant in bioleaching. Bioleaching results revealed that higher strain growth led to a higher copper recovery, while higher pulp density will cause a greater inhibitory effect on strain growth and ammonia production. The copper extraction reached the highest value of 54.5% at the pulp density of 1%. Malachite, chrysocolla and chalcocite are easy to leach out in this bioleaching system while chalcopyrite is difficult. Results of comparative leaching experiments show that bioleaching using JAT-1 is superior to ammonia leaching at the same condition. The metabolites produced by the strain other than ammonia are also involved in bioleaching.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
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 head0.020
GPT teacher head0.227
Teacher spread0.208 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2016
Admission routes1
Has abstractyes

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