MétaCan
Menu
Back to cohort

Mechanical Grading of Structural Larch Dimension Lumber

2012· article· en· W1978327882 on OpenAlex
Wan Li Lou, Hai Qing Ren, Zhao Hui Wang, Xiu Qin Luo

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueKey engineering materials · 2012
Typearticle
Languageen
FieldEngineering
TopicWood Treatment and Properties
Canadian institutionsnot available
FundersMinistry of Education, IndiaFPInnovationsMinistry of Earth Sciences
KeywordsLarchGrading (engineering)Flexural strengthYoung's modulusUltimate tensile strengthMaterials scienceStructural engineeringComposite materialEngineeringCivil engineering

Abstract

fetched live from OpenAlex

Larch dimension lumber bending strength properties from full-size bending test were used to establish preliminary grade boundary settings for mechanical grading of lumber by modulus of elasticity. Simulated production using the grade boundary settings were evaluated for modulus of rupture, ultimate tensile strength, and ultimate compressive strength. The results showed a good relationship between modulus of rupture and modulus of elasticity, and the observed relationships between strengths properties were consistent with that assumed for the standard grades. Through mechanical grading, larch dimension lumber could be sort grades: M14, M30 and M40. Assuming the visual requirements are met, the M30 and M40 grades account for more than 80% of the total production. Mechanical grading of larch appears to be a viable approach for grading Chinese large for structural applications.

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.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.012
GPT teacher head0.198
Teacher spread0.186 · 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