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Record W4287391917 · doi:10.5281/zenodo.5115842

The Effect of Yarding Technique on Yarding Productivity and Cost: Conventional Single-Hitch Suspension vs. Horizontal Double-Hitch Suspension

2021· article· en· W4287391917 on OpenAlexaff
Raffaele Spinelli, Natascia Magagnotti, Giulio Cosola, Eric R. Labelle, Rien Visser, Gernot Erber

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicTurfgrass Adaptation and Management
Canadian institutionsUniversité Laval
FundersEuropean Commission
KeywordsSuspension (topology)ProductivityMaterials scienceMathematicsEconomics

Abstract

fetched live from OpenAlex

Cable yarding is a well establish technology for the extraction of timber in steep terrain. How ever, it is encumbered with relatively low productivity and high costs, and as such this technol ogy needs to adapt and progress to remain viable. The development of biomass as a valuable byproduct, and the availability of processors to support yarder operations, lend themselves to increasing the level of whole-tree extraction. Double-hitch carriages have been developed to allow for full suspension of whole-tree and tree-length material. This study compared a stan dard single-hitch to a double-hitch carriage under controlled conditions, namely in the same location using the same yarder with downhill extraction. As expected, the double-hitch carriage took longer to load up (+14%), but was able to achieve similar productivity (10–11 m3 per productive machine hour) through increased inhaul speed (+15%). The importance of this study is that it demonstrates both the physical and economic feasibility of moving to whole-tree extraction using the double-hitch type carriage for longer corridors, for settings with limited deflection, or areas with lower tolerance for soil disturbance

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)0.001
metaresearch head score (Gemma)0.003
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.024
GPT teacher head0.239
Teacher spread0.215 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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