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Recuperación de minerales aurífero mediante flotación, provenientes del sector la Pangui, distrito minero Chinapintza – Ecuador

2020· article· es· W3024797646 on OpenAlexvenueno aff
Angel Fabián Chicaiza Ortiz

Bibliographic record

VenueConcienciaDigital · 2020
Typearticle
Languagees
FieldEconomics, Econometrics and Finance
TopicBusiness, Innovation, and Economy
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesChemistryArt

Abstract

fetched live from OpenAlex

Este trabajo de investigación se divide en tres fases, las mismas que sirven para determinar distintos balances metalúrgicos. La primera fase abarca ensayos de molienda para obtener los tamaños de grano deseados (84µm, 74µm y 67µm), posteriormente se realizan ensayos de flotación con estos tamaños, encontrando como mejor tamaño de grano al de 84µm por el rendimiento de esta recuperación, 76.54%. La segunda fase consta de la variación del pH del medio en el que se desarrolla la flotación (8, 9 y 10), encontrando el mejor resultado en el pH 10 con un rendimiento de 88.19%. La tercera fase se realiza con variaciones en la concentración del colector (75g/ton, 50g/ton y 100g/ton), dando como resultado la mejor recuperación a la flotación con la concentración del colector de 100g/ton debido a su rendimiento, 76.54%.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.029
GPT teacher head0.216
Teacher spread0.187 · 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 designBench or experimental
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

Citations1
Published2020
Admission routes1
Has abstractyes

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