MétaCan
Menu
Back to cohort
Record W2166172903 · doi:10.15376/biores.6.4.3960-3972

Binderless panels made with black spruce bark

2011· article· en· W2166172903 on OpenAlex
Zhenhua Gao, Xiangming Wang, Hui Wan, Gilles Brunette

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBioResources · 2011
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsFPInnovations
FundersFundamental Research Funds for the Central Universities
KeywordsFiberboardMaterials scienceFlexural strengthBark (sound)Composite materialPressingHomogeneousBlack spruceOriented strand boardPulp and paper industryEngineeringMathematicsForestry

Abstract

fetched live from OpenAlex

The bark of black spruce was thermo-mechanically refined and used to manufacture binderless bark-based fiberboard with various pressing temperatures, times, and panel structures in order to utilize an abundant bark resource for a better value-added application. The test results indicated that it is technically feasible to manufacture binderless fiberboard with refined black spruce bark through self-bonding under elevated temperatures over a reasonable period of pressing time. Binderless bark-based fiberboards with a homogeneous structure had very poor flexural properties due to the poor strength of bark itself; however, by using a sandwich structure with 30wt% wood fiber in the surface layers and 70wt% bark in the core layer it was possible to sufficiently improve panel flexural properties so that the manufactured binderless bark-based fiberboards was able to meet the mechanical property requirements of 115-grade fiberboard according to ANSI A208.2 (2009). Refining conditions had a great impact on the mechanical properties of binderless bark-based fiberboard.

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 categoriesInsufficient payload (model declined to judge)
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 score0.012
Threshold uncertainty score1.000

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.026
GPT teacher head0.219
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