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Record W4410740789 · doi:10.1139/cjfr-2025-0063

Comparing operational and economic viability of integrated full tree roundwood and residue harvesting with tree length roundwood harvesting in a shelterwood system

2025· article· en· W4410740789 on OpenAlexafffundvenueabout
Patrick A. Levasseur, Nathan Basiliko, John P. Caspersen, Jeff Fera, Trevor A. Jones

Bibliographic record

VenueCanadian Journal of Forest Research · 2025
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsLakehead UniversityUniversity of TorontoNatural Resources Canada
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsForestryEnvironmental scienceTree (set theory)MathematicsGeography

Abstract

fetched live from OpenAlex

Integrating roundwood harvesting with collecting traditionally unmerchantable harvest residue largely depends on the operational and economic viability for harvest contractors. This study compared machine time of motion, harvest volumes, productivity, and profits of two harvesting methods in a shelterwood system in the Canadian Great Lakes-St. Lawrence (GLSL) forest region. Conventional “tree length” (TL) harvesting only harvested merchantable roundwood and “full tree” (FT) harvesting residues and roundwood. FT harvesting required significantly more feller buncher time of motion compared to TL harvesting, but there were no significant differences in time of motion for other machines. FT harvesting yielded greater volumes of residue (34.1 ± 6.9 m 3 ha −1 ), small poles, and medium poles compared to TL. Average profits were 36% higher using FT harvesting ($611 ± 560 CAD ha −1 ), compared to TL ($450 ± 506 CAD ha −1 ), but these differences were not statistically significant. The increased profits at FT blocks were largely from residual management services (such as chipping) versus the sale of residues for bioenergy feedstocks. The results from this study suggest that FT harvesting recovers greater merchantable volumes and can be equally or more profitable at the contractor level than TL harvesting in GLSL shelterwood systems.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score0.980

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 head0.029
GPT teacher head0.258
Teacher spread0.229 · 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

Citations1
Published2025
Admission routes4
Has abstractyes

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