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Record W2149955828 · doi:10.1139/x05-300

Effects of forest roads on the growth of adjacent lodgepole pine trees

2006· article· en· W2149955828 on OpenAlexvenueno aff
Michael Scott Bowering, Valerie LeMay, Peter Marshall

Bibliographic record

VenueCanadian Journal of Forest Research · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
FundersMinistry of Environment
KeywordsBasal areaPinus contortaForestryDiameter at breast heightRange (aeronautics)Environmental scienceBiologyGeography

Abstract

fetched live from OpenAlex

The effects of roads on growth of adjacent lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia Engelm.) trees were studied in an area near Williams Lake, British Columbia. Plots were established in a range of stand ages, site qualities, stand densities (stems/ha), stand basal areas (m2/ha), edge aspects, and adjacent road widths. Plots were divided into five zones beginning at the road edge: 0–5 m (zone 1), 5–10 m, 10–20 m, 20–30 m, and 30–40 m (zone 5) from the edge. When the 5 years prior to road establishment were used to scale growth rates, relative tree basal growth rates for zone 1 differed significantly from those for the other zones, with zone 1 rates being 32.1% higher, on average, for 3 to 15 years after the road opening was established. Fewer dead standing trees were found nearest the road edge. Tree bole shapes at breast height were not significantly different among zones, and no significant increases in average basal area per tree or in average height were found. On average, zone 1 had a 31.3% greater stand basal area than zone 5. For a 23.4 m wide road, the increased stand basal area translates to 3.13 m (2 × 31.3% × 5 m for zone 1), or 13.4% recovery of timber losses, if both sides of the road were similarly impacted. This estimate can be improved by using stand basal area to reduce variability.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.895
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.246
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), 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
Published2006
Admission routes1
Has abstractyes

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