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Record W1943991695 · doi:10.1002/atr.1193

Improved rehandling strategies for the container retrieval process

2012· article· en· W1943991695 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Advanced Transportation · 2012
Typearticle
Languageen
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsContainer (type theory)HeuristicsFlexibility (engineering)Computer scienceReliability (semiconductor)Operations researchProcess (computing)Branch and boundHeuristicWork (physics)Mathematical optimizationVolume (thermodynamics)Sequence (biology)Transport engineeringEngineeringAlgorithmMathematicsMechanical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

SUMMARY The breakdown of trade barriers among countries led to an enormous increase in the volume of international trade. Consequently, the amount of bulk cargo carried in containers and transported overseas exploded because of flexibility and reliability of this type of transportation. So, the efficiency in container terminals has emerged as a major problem. In this work, we deal with an operational problem in container terminals. We try to develop strategies to minimize the total time required to retrieve the containers from a bay in a predetermined sequence. The problem is solved optimally using a branch and bound‐based algorithm, and alternative heuristics that give near‐optimal solutions are proposed. Copyright © 2012 John Wiley & Sons, Ltd.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.835
Threshold uncertainty score0.192

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 head0.012
GPT teacher head0.257
Teacher spread0.245 · 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