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Record W2113720160 · doi:10.1186/1179-5395-43-7

Effects of curvature in hybrid poplar on acoustic velocity at the tree level

2013· article· en· W2113720160 on OpenAlexaff
Normand Paradis, Glen Murphy

Bibliographic record

VenueNew Zealand journal of forestry science · 2013
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsUniversité Laval
FundersOregon State University
KeywordsCurvatureTree (set theory)Regular polygonDisplacement (psychology)MathematicsGeologyGeometryAcousticsPhysicsMathematical analysis

Abstract

fetched live from OpenAlex

Measurements of stress wave velocity were performed with the Fibre-gen Director ST300 on 146 hybrid poplar ( Populus sp.) plantation trees from GreenWood Resources Inc. located near the city of Boardman, Oregon USA. A laser scanner (Faro Focus 3D) was used to measure the curvature of the trees. Combinations of two software tools (Treemetrics Autostem and Sweep Extractor) were used to calculate the curvature for two log lengths (3 and 6 m) from a height of 10 cm above the ground. The propagation velocities of the stress waves were measured twice; first at breast height on the convex side of the stem and then at 90 degrees clockwise around the stem from the first measurement. Analysis of the data showed that there was no significant difference (p = 0.24) between propagation velocities when the probes were located on either the convex side or at 90 degrees to the convex side. However, the propagation velocity was significantly greater on trees with higher curvature, suggesting that the velocities measured with the ST300 can take into account the presence of reaction wood (tension wood) in the tree.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.204
Teacher spread0.193 · 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 designBench or experimental
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

Citations5
Published2013
Admission routes1
Has abstractyes

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