MétaCan
Menu
Back to cohort
Record W2131738550 · doi:10.1080/17480930701482961

Operator and dipper tooth influence on electric shovel performance during oil sands mining

2008· article· en· W2131738550 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Mining Reclamation and Environment · 2008
Typearticle
Languageen
FieldEngineering
TopicMineral Processing and Grinding
Canadian institutionsUniversity of AlbertaSyncrude (Canada)
FundersSyncrude
KeywordsShovelDiggingHoist (device)DipperEngineeringMining engineeringOperator (biology)Mechanical engineeringGeographyArchaeology

Abstract

fetched live from OpenAlex

A shovel performance monitoring study was undertaken in two oil sands mines operated by Syncrude Canada Ltd. using performance data obtained from P&H 4100 TS and BOSS electric mining shovels. One year of shovel performance data along with geological, geotechnical and climatic data were analysed. The approach adopted was to use current and voltage data collected from hoist and crowd motors to first identify dig cycles and then to calculate the energy and/or power associated with digging. Analysis of performance data while digging uniform material along with operator team schedules showed that the performance of a shovel can vary significantly depending on which operator is digging. Up to 25% variability in hoist power consumption and 50% variability in productivity was noted between different operators. Shovel type and dipper teeth configuration can also influence the power draw on electrical motors during digging.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.692
Threshold uncertainty score0.359

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 head0.008
GPT teacher head0.194
Teacher spread0.185 · 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