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Record W2888374574 · doi:10.1139/cjfr-2018-0269

Behavior and assessment of mobile anchors in cable yarding systems

2018· article· en· W2888374574 on OpenAlexvenueno aff
Anthony Mancuso, Francisca Belart, Ben Leshchinsky, M.L. Russell, James D. Kiser

Bibliographic record

VenueCanadian Journal of Forest Research · 2018
Typearticle
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAnchoringEmbedmentSkylineStructural engineeringEngineeringComputer scienceCivil engineering

Abstract

fetched live from OpenAlex

Cable yarding systems transport logs hoisted off the ground by a system of cables and a carriage that moves along the cable. Ground anchors are used at the end points to maintain sufficient tension between the two ends points of the cable and keep it in the air during operation. Traditional anchoring methods employ tree stumps, but as shorter stand rotations result in younger weaker stumps and because of the inability to visually inspect root structures to calculate a stump’s anchoring capacity, alternative methods of anchoring are being used more frequently. In this paper, the capacity of an alternative anchoring method, known as equipment anchoring or mobile anchoring, is assessed for guyline and skyline applications. Some critical components that are observed to influence anchor capacity are equipment weight, slope, blade embedment, angle of cable pull, and soil type. An analytical design solution for mobile anchor capacity is compared with the results of over two dozen field tests to determine the effectiveness of predicting anchor capacity. In addition to estimating the capacity of mobile anchors, a relationship between skyline and guyline loading recorded on harvest operations is analyzed.

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 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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.632
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.058
GPT teacher head0.357
Teacher spread0.299 · 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 teacher head, 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
Published2018
Admission routes1
Has abstractyes

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