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Record W4411151487 · doi:10.1007/s44346-025-00007-2

Deviation to mucking analysis

2025· article· en· W4411151487 on OpenAlexafffund
Coralee Heske, Derek B. Apel, Muhammad Adil, Bunyamin Kahraman, Wei Victor Liu, Yuanyuan Pu

Bibliographic record

VenueDiscover Minerals. · 2025
Typearticle
Languageen
FieldEngineering
TopicMining Techniques and Economics
Canadian institutionsVale (Canada)University of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsGeodesyMeteorologyGeologyEnvironmental scienceMathematicsGeography

Abstract

fetched live from OpenAlex

Deviations in drill holes during underground mining substantially impact operating efficiency and safety. An examination of six stopes at Thompson Mine revealed systematic deviations of roughly 1 m (3 ft) from the designated drill hole configuration. Excessive deviations may result in imprecise ore body targeting, elevated operational expenses, and possible safety risks. A correlation was identified between the percentage of holes deviating beyond the average and the mucking rate, indicating that to sustain an average of 544 tonnes (600 tons) per day, less than 21% of holes should surpass the average deviation. It is also essential that drill speeds average 9.14 m (30 ft) per hour, particularly for novice operators, to reduce hole deviation. Regulated drilling rates improve precision, resulting in more efficient and economical operations. Decreased drilling rates mitigate the likelihood of misalignment and structural complications. Efficient management of drill hole variations necessitates sophisticated drilling tools, meticulous planning, and ongoing monitoring to guarantee adherence to the designated drill trajectory. Mining operations can achieve improved resource recovery, decrease waste, and enhance production by limiting deviations. Mitigating drill hole deviations is crucial for enhancing underground mining projects' economic and environmental results, allowing for a degree of tolerance for systematic deviations. Supplementary Information: The online version contains supplementary material available at 10.1007/s44346-025-00007-2.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.299

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.232
Teacher spread0.226 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes2
Has abstractyes

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