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Record W3004296832 · doi:10.14288/1.0376466

The Potential Effects of Tethered-Based Forest Harvesting Systems on Soil Disturbance in Coastal British Columbia

2019· article· en· W3004296832 on OpenAlexaboutno aff
Jacob Atherton

Bibliographic record

VenuecIRcle (University of British Columbia) · 2019
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsnot available
Fundersnot available
KeywordsDisturbance (geology)Environmental scienceAgroforestryHydrology (agriculture)Remote sensingEnvironmental resource managementGeographyForestryGeologyGeotechnical engineeringGeomorphology

Abstract

fetched live from OpenAlex

While there are a variety of forest harvesting systems, there are few able to operate within the constraints of the increasingly harder to access timber supplies found in the mountains of the coastal region of British Columbia. While ground-based systems can access the more flat, unbroken terrain, steeper mountainous areas are limited to aerial-based systems, cable-based systems, and tethered-based systems, the latter being the focus of this paper. Tethered-based systems, namely tethered feller-bunchers, operate through working with a winching system upslope from the machine that allow for increased traction when operating (Sessions et al., 2017). This allows for these machines to operate on steeper slopes than their untethered counterparts (Sessions et al., 2017). While this method is generally more efficient and cost-effective compared to cable-based and aerial-based systems, it is important to note the effect of these machines on the soils in which they operate (Sessions et al., 2017). This paper assesses the issues associated with operating this machinery on steeper slopes. Research and knowledge on soil disturbance using ground-based harvesting methods, coupled with steep slope soil characteristics, will be used to examine the potential negative effects for harvesting with tethered-based system on soil disturbance, soil compaction, and slope stability.

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)0.000
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 score0.659
Threshold uncertainty score0.724

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.003
GPT teacher head0.138
Teacher spread0.135 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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