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Record W2141388494 · doi:10.22260/isarc2009/0013

An Overview of Autonomous Loading of Bulk Material

2009· article· en· W2141388494 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.

Bibliographic record

VenueProceedings of the ... ISARC · 2009
Typearticle
Languageen
FieldEngineering
TopicBelt Conveyor Systems Engineering
Canadian institutionsMcGill University
Fundersnot available
KeywordsDownloadComputer scienceHeap (data structure)AutomationWork (physics)Process (computing)EngineeringWorld Wide WebMechanical engineeringOperating system

Abstract

fetched live from OpenAlex

Autonomous loading implies a fully automated scenario in which automated excavating machines, such as front-loaders, load themselves from a heap of bulk material and deliver to the dumping site. The process comprises all the functions of loading, navigating, obstacle detection and avoidance and unloading to be automated and controlled by a supervisory computer. Autonomous loading benefits a number of industries such as construction and mining, from economical view point as well as other concerns like operators safety when the workplace is not hazard free. Despite all the benefits and despite considerable amount of research on the subject, there are no commercially available systems that can be purchased and put to work. In addition to a breakdown of all the tasks that need to be automated and the difficulties involved, this paper reviews and reports the various research and/or development activities that have been carried out during the past two decades on the subject.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.563

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 head0.020
GPT teacher head0.235
Teacher spread0.216 · 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