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Record W2898618705

RESEARCH ON USING PLYWOOD MADE FROM DOMESTIC SPECIES OF WOOD FOR LONGBOARD MANUFACTURING

2016· article· en· W2898618705 on OpenAlexaboutno aff
Adriana Fotin, Aurel Lunguleasa, Camelia Coșereanu, Luminiţa-Maria Brenci

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicUrban and spatial planning
Canadian institutionsnot available
Fundersnot available
KeywordsPulp and paper industryForestryMathematicsEnvironmental scienceEngineeringGeography
DOInot available

Abstract

fetched live from OpenAlex

The paper presents the results of the experimental research on replacing the actual plywood generally used for longboards manufacturing with plywood made from domestic species of wood, namely beech and birch wood. The research is focused on the determination of bending deformation, modulus of rupture (MOR) and modulus of elasticity (MOE) for bending strength of the investigated plywood specimens according to SR EN 310-1996, and also of the longboards made from the three types of plywood. The research was conducted on standard specimens made from beech and birch plywood on one hand, and on specimens made from a combination of bamboo and Canadian maple plywood, on the other hand, having the sizes and testing position according to SR EN 310-1996. In addition, boards having sizes and shapes of actual longboards were also tested for bending deformation, using in all cases a constant distance of 500mm between supports when applying the force, equals to the distance between track rollers of longboard. The conclusion of the research is that the domestic plywood made from beech and birch wood could replace in the future the actual plywood used in the longboard manufacturing

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)0.001
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.430
GPT teacher head0.559
Teacher spread0.129 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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