MétaCan
Menu
Back to cohort
Record W4395962243 · doi:10.18280/jesa.570221

Optimizing Time in Horizontal Mining Excavations: 10 Formats Inspired by Value Stream Mapping Principles

2024· article· en· W4395962243 on OpenAlexvenueno aff
Johnny Henrry Ccatamayo Barrios, Luis Miguel Soto Jusacamayta, Kelvis Berrocal Argumedo, Jaime Cesar Mayorga Rojas, Walter Javier Díaz Cartagena, Roberto-Juan Gutiérrez Palomino, H. David Calderón Vilc

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2024
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsExcavationValue stream mappingValue (mathematics)Computer scienceMining engineeringGeologyData miningGeotechnical engineeringEngineeringMachine learningOperations management

Abstract

fetched live from OpenAlex

In this research, 10 formats are proposed as a single instrument, for taking mining cycle times based on the principles of the value chain map tool.As part of the methodology, we applied the 10 proposed formats in time recording, we analyzed the productive, contributory and non-contributory times, for this we used the Ishikawa diagram that shows us which activities have the greatest impact on low productivity in the horizontal excavations (Mining Cycle) later the mapping of the current state was carried out, finally the time efficiency was processed and calculated.To validate the results, we applied the proposal in the Esmeralda mining company, in its El Teniente mine for 4 months.As a result, we revealed that from a total cycle time of 22.85 hours: the productive time was 13.22 hours, the Contributory time was 4.53 hours and non-contributory time was 5.11 hours.We also revealed that the activities that show the lowest performance are: ventilation, marine loading, coining and drilling.The final conclusion is based on the contribution of the 10 formats and is applicable to underground mining companies who can use the 10 proposed formats to analyze productive, contributory and non-contributory times, and then take corresponding actions.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.820
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.021
GPT teacher head0.230
Teacher spread0.209 · 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 teacher head, not a consensus.

Study designSimulation or modeling
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal Européen des Systèmes AutomatisésSame topicMining Techniques and EconomicsFrench-language works237,207