MétaCan
Menu
Back to cohort
Record W2128588305 · doi:10.5402/2012/923413

Recycled Old Corrugated Container Fibers for Wood-Fiber Cement Sheets

2012· article· en· W2128588305 on OpenAlexaboutno aff
Divino Eterno Teixeira

Bibliographic record

VenueISRN Forestry · 2012
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsnot available
Fundersnot available
KeywordsPinus radiataPulp (tooth)Kappa numberKraft processRadiataPulp and paper industryKraft paperFiberComposite materialMaterials scienceSoftwoodBotanyDentistry

Abstract

fetched live from OpenAlex

Recycled pulp of old corrugated containers (OCCs) was studied as a possible fiber source for wood-fiber cement (WFC) sheets. This industry currently largely relies on kraft pulp, an expensive fiber source. Thus, WFC sheets were made using less costly OCC fibers utilizing various types of treatments including fiber fractionation (FF) and refining to 500 and 400 Canadian Standard Freeness (CSF) to determine the effect of these treatments on the sheets strength. Unprocessed virgin kraft fiber of radiata pine ( Pinus radiata ) was used as control for comparison purposes. The unprocessed OCC pulp furnish yielded an initial freeness of 635 CSF; after fractionating, however, it was increased to 754 CSF. OCC pulp presented a kappa number of 47, compared to 23.7 for P. radiata . Fractionating OCC pulp was effective in raising the range of long fibers in the stock from 68 percent to 85 percent, before and after fractionating, respectively. Results from WFC sheets made in laboratory showed that there was no significant difference in board strength among boards made with unprocessed OCC fibers, fractionated and refined OCC, and P. radiata fibers. However, sheet strength decreased when refined OCC fibers were used. The results suggest that OCC fibers can produce WFC sheets with desirable characteristics compared to those of unrefined virgin kraft fiber of P. radiata .

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 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 score0.038
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 science0.0000.000
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 head0.017
GPT teacher head0.262
Teacher spread0.245 · 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 teacher head, not a consensus.

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

Citations6
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueISRN ForestrySame topicNatural Fiber Reinforced CompositesFrench-language works237,207