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Record W2607494215 · doi:10.2989/20702620.2016.1275836

The impact of log surface damage caused by harvester<i>Eucalyptus</i>debarking on pulp value recovery

2017· article· en· W2607494215 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueSouthern Forests a Journal of Forest Science · 2017
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsLakehead University
Fundersnot available
KeywordsEucalyptusPulp (tooth)Environmental scienceMathematicsPulp and paper industryForestryBotanyBiologyEngineeringGeographyMedicineDentistry

Abstract

fetched live from OpenAlex

Mechanised harvesting operations are growing in popularity in South Africa, as motor-manual and manual harvesting operations pose significant health and safety risks to workers. Potential damage inflicted by single-grip harvester feed rollers and delimbing knives on the log surface during debranching and debarking of eucalypts may affect chip size distributions during chip production. Chip size is important as it influences pulp quality and recovery in the kraft pulping process. The study investigated the influence of two mechanised debarking treatments in eucalypts (three feed roller passes and five feed roller passes along the stem surface) with feed-roller-induced log surface damage on pulp value recovery. The two mechanised treatments were compared against chips produced from manually debarked logs with no surface damage. In addition, the effect of two log drying periods (one week and two weeks) and three log classes (base, middle and top logs) on chip quality were also analysed. An economic evaluation was conducted to quantify potential recoverable pulp value losses associated with debarking treatments and log drying periods. Logs subject to manual debarking produced significantly less undesired sized chips than both three-pass and five-pass mechanically debarked logs and therefore had significantly greater pulp value recovery. Mechanically debarked logs had a projected pulp value recovery of R62, R86 and R123 less per bone dry tonne of chips produced from base, middle and top logs, respectively, when compared with manually debarked logs with no log surface damage after a one-week log drying period. Mechanically debarked logs also had a projected pulp value recovery of R77, R40 and R59 less per bone dry tonne of chips produced from base, middle and top logs, respectively, when compared with manually debarked logs with no log surface damage after a two-week log drying period. Logs dried for two weeks also produced significantly less under-sized chips than chips produced from one-week-dried logs and therefore had greater pulp value recovery. However, two-week-dried logs produced wood chips with significantly more over-thick chips than logs dried for one week. The volume of undesirable-sized chips produced during chipping increased with decreasing log size.

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.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.000
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 head0.013
GPT teacher head0.263
Teacher spread0.250 · 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