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Record W2884552071 · doi:10.1002/pts.2385

The influence of stretch wrap containment force on load bridging in unit loads

2018· article· en· W2884552071 on OpenAlexaff
Jonghun Park, László Horváth, Marshall S. White, Philip A. Araman, Robert J. Bush

Bibliographic record

VenuePackaging Technology and Science · 2018
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsToronto Metropolitan University
FundersU.S. Forest ServiceU.S. Department of Agriculture
KeywordsPalletStiffnessBridging (networking)Deflection (physics)Structural engineeringUnit loadEngineeringMechanical engineeringComputer sciencePhysics

Abstract

fetched live from OpenAlex

The term “load bridging” describes a phenomenon in which the physical interaction between various packaging components acts as a series of discrete loads in a given unit load and adds stiffness to the shipping pallet/load combination. Current pallet design practices often ignore the aspect of load bridging and assume that the pallet payload is flexible and uniformly distributed over the pallet surface. This can influence the load‐carrying capacity of the pallet. The study reported in this paper investigated the relationship between the stretch wrap containment force and load bridging in unit loads and the resulting unit‐load deflection. The experimental results of this study indicate that an increase in the stretch wrap containment force can improve the unit‐load deflection by as much as 81%. The influence of the stretch wrap containment force on pallet deflection is greatest for small packages and pallets with low stiffness. These experimental results provide useful information for realizing more efficient and sustainable unit‐load designs.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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)0.0030.000

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 head0.009
GPT teacher head0.246
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations8
Published2018
Admission routes1
Has abstractyes

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