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Record W3127338109 · doi:10.3390/iecf2020-08078

Evaluation of Wood Chipping Efficiency through Long-Term Monitoring

2020· article· en· W3127338109 on OpenAlexaff
Alberto Cadei, Luca Marchi, Omar Mologni, Raffaele Cavalli, Stefano Grigolato

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsEnvironmental scienceTruckGNSS applicationsLoggingBiomass (ecology)Computer scienceAutomotive engineeringGlobal Positioning SystemForestryEngineeringGeologyTelecommunications

Abstract

fetched live from OpenAlex

A high volume of wood forest biomass is be available at the roadside when whole three (WT) harvesting systems are applied. Besides, salvage logging operations are favourable conditions to accumulate a large amount of low-quality biomass due to the recovery of damaged trees. In mountain regions, such as the Alps, the forest’s accessibility can be a significant constraint for the eco-efficiency of chipping operations. The present study aims at evaluating the efficiency of wood-chipping operations in mountain areas based on long-term monitoring. One chipper-truck was monitored over 1200 working hours using telemetry. Different efficiency parameters were collected: machine position, collected using Global Navigation Satellite System (GNSS) receiver, and engine parameters, collected using the CAN Bus system based on J 1939. Efficiency parameters were used to compare different in-wood or landing configurations. The results show the influence of the different location of the chipping sites according to the road network. Chipping operations in space-constrained sites cause an increase in delay time and CO2 emissions.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.179
Threshold uncertainty score0.241

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 head0.066
GPT teacher head0.289
Teacher spread0.223 · 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 designSimulation or modeling
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

Citations7
Published2020
Admission routes1
Has abstractyes

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