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Record W39517319 · doi:10.3390/healthcare12212106

Synthetic rope applications in Appalachian logging

2008· article· en· W39517319 on OpenAlexaboutno aff
B. Spong, Jingxin Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsnot available
FundersJapan Science and Technology AgencyJuntendo University
KeywordsRopeLoggingWire ropeEngineeringFlexibility (engineering)Environmental scienceForestryStructural engineeringMathematicsGeography

Abstract

fetched live from OpenAlex

New ultra-high molecular weight polyethylene rope has shown good results as a replacement for wire rope in logging applications in the western United States. A single case study trial was performed in Appalachian forest conditions to assess the appropriateness of this technology for hardwood logging applications. The study focused on use of the rope in West Virginia and included a review of the legal status for using synthetic rope in logging and informal interviews with loggers and forest managers to understand which applications might be appropriate and what specifications are required for length, strength, and other characteristics. A 125-foot length of 5/8-inch synthetic rope was purchased and installed on a bulldozer for this case study. The synthetic rope has a premium price almost double that of wire rope. Such potential benefits as improved worker conditions, greater productivity, and decreased negative environmental impacts may lead to wide adoption and eventually lower prices. Trials were observed in a logging operation and road-clearing operation in which the rope performed well and loggers were impressed by the strength, ease of splicing, light weight, and flexibility of the rope.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.019
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.010
GPT teacher head0.193
Teacher spread0.183 · 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 designObservational
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

Citations2
Published2008
Admission routes1
Has abstractyes

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