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Record W3095118448 · doi:10.1080/14942119.2021.1826882

Mechanical response of natural anchors in cable logging

2020· article· en· W3095118448 on OpenAlexaff
Luca Marchi, Davide Trutalli, Omar Mologni, Raimondo Gallo, Dominik Röeser, Raffaele Cavalli, Stefano Grigolato

Bibliographic record

VenueInternational Journal of Forest Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicTree Root and Stability Studies
Canadian institutionsUniversity of British Columbia
FundersRegione Lombardia
KeywordsAnchoringSkylineLoggingStructural engineeringTree (set theory)Computer scienceEngineeringCivil engineeringMathematics

Abstract

fetched live from OpenAlex

Cable logging is a common harvesting technique for steep slope conditions, despite the safety hazard for the operators, which is mainly related to failure of cables or anchor trees. The multiple factors that determine whether a tree should be considered as an anchor make it difficult to estimate the actual suitability of the anchor tree from a safety perspective during logging. To address this critical question, the mechanical response of natural anchors was monitored in nine cable logging sites using a standing skyline system. Different anchoring methods were observed, namely, tieback anchors, multiple in-line anchors and single tree anchors. Based on previous experience regarding tree stability assessment, an innovative continuous monitoring technique was applied at each research site. The mechanical response of the anchors was analyzed in terms of force-rotation curves, which summarize the effects of loads transmitted by the skyline. An elastic response was observed in nearly all anchors, but the magnitude of the rotation varied depending on the anchoring method and the applied force observed during the survey. Effects due to cyclic loading were analyzed in four of the case studies, and an apparent relaxation phenomenon at the root-plate system was observed. Finally, the research provides an estimation of the anchor’s stability with respect to their maximum theoretical resistance, evaluated from trees showing similar characteristics that have previously been tested. Results from these field measurements provide information that can improve guidance regarding the holding strength of anchors to current empirical methods and help ensure safety anchoring methods.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.010
GPT teacher head0.221
Teacher spread0.212 · 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

Citations10
Published2020
Admission routes1
Has abstractyes

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