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Record W2996710343 · doi:10.1139/cjfr-2019-0338

Deadman anchoring design for cable logging: a new approach

2019· article· en· W2996710343 on OpenAlexvenueno aff
Francisca Belart, Ben Leshchinsky, Jeff Wimer

Bibliographic record

VenueCanadian Journal of Forest Research · 2019
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsnot available
Fundersnot available
KeywordsAnchoringLoggingTerrainVariety (cybernetics)Resistance (ecology)ForestryPerspective (graphical)Douglas firEngineeringEnvironmental scienceComputer scienceStructural engineeringGeographyEcologyCartography

Abstract

fetched live from OpenAlex

Cable yarding is still a common system for transporting wood in steep terrain. In the Pacific Northwest, United States, and other regions of high-productivity forestry, reduced rotation ages for harvest have resulted in a lack of large stumps to serve as anchors for cable-yarding systems. One of the most common anchoring alternatives to stumps is buried deadman anchors. Conventional design of this system has been limited to simplified charts that account for soil resistance, as well as both shear and bending resistance, of the deadman, which is typically a buried log. However, these charts are limited to larger deadman anchors of only Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), which are likely not readily available in modern operations. Thus, revised, simplified design charts are proposed that consider a variety of different soil failure mechanisms, as well as several different wood types and bending conditions. An updated approach provides a quantitative perspective towards safe anchoring in modern forest operations.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.787
Threshold uncertainty score0.567

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 head0.104
GPT teacher head0.303
Teacher spread0.199 · 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 designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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