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Record W2139140081 · doi:10.5558/tfc2013-087

Effects of precommercial thinning on the forest value chain in northwestern New Brunswick: Part 2 – Efficiency gains in cut-to-length harvesting

2013· article· en· W2139140081 on OpenAlexaffvenueabout
Jean Plamondon, Doug Pitt

Bibliographic record

VenueThe Forestry Chronicle · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsCanadian Forest ServiceFPInnovations
Fundersnot available
KeywordsThinningAbies balsameaBalsamForestryMathematicsProductivityEnvironmental scienceStatisticsBotanyGeographyBiologyEconomics

Abstract

fetched live from OpenAlex

The Green River precommercial thinning trials were established between 1959 and 1961 in naturally regenerating balsam fir (Abies balsamea [L.] Mill.)-dominated stands an average of eight years after overstory removal. Three nominal spacings of 4 ft (1.2 m), 6 ft (1.8 m) and 8 ft (2.4 m) were compared to an unthinned control in six replicate blocks. In the fall of 2008, following completion of the ninth sequential evaluation of the study’s 48 permanent sample plots, three of the six replicates were clearcut harvested using a cut-to-length system; data were collected on harvesting, forwarding and loading efficiency. Largely due to increased average tree size, precommercially thinned stands were associated with improvements in both harvesting and forwarding productivity, proportional to thinning intensity. For example, the nominal 6-ft spacing yielded 30% to 35% gains in harvesting productivity, and 16% to 39% gains in forwarding productivity (p ≤ 0.04), these ranges spanning the different levels of operator expertise and block conditions observed. In total, estimated harvesting costs were $12.58/m 3 in the unthinned stands, compared to $9.10/m 3 in the 6-ft spaced stands (p = 0.02). Such a difference represents a $3.48/m 3 savings, which translates into just over $1000 per ha, based on the gross merchantable volume produced in the 6-ft spaced stands.

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 score0.300
Threshold uncertainty score0.981

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.227
Teacher spread0.216 · 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

Citations22
Published2013
Admission routes3
Has abstractyes

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