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Record W2373756492

Buğday Saplarinin Kompozit Levha Üretiminde Kullanilmasi

2002· article· tr· W2373756492 on OpenAlexaboutno aff
Fatih Mengeloğlu, Mehmet Hakkı Alma

Bibliographic record

Venuenot available
Typearticle
Languagetr
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsnot available
Fundersnot available
KeywordsMedium density fiberboardFiberboardProduction (economics)Environmental scienceStrawAgricultureWaste managementRaw materialPulp and paper industryBusinessAgricultural engineeringEngineeringMaterials scienceComposite materialCivil engineeringAgronomy
DOInot available

Abstract

fetched live from OpenAlex

There is a growing pressure on finding alternative fiber resources due to decrease in available forest resources, rising of timber prices, and growing environmental pressure on timber harvesting. This subject has gotten the attention of many scientists in well-developed countries. Specifically, in countries like Canada with its huge forest resources and large volumes of available wood residues for the composite industry, there is an increased interest in the use of agricultural residues for composite panel manufacture. The successful production of wheat straw-based composites has been established after extensive technological developments to overcome the handling and processing problems. Resulting composites are high quality products that can stimulate the consideration and development of other value-added building materials using agricultural residues. Considering the annual production and availability of wheat straw in Turkey, there is a great potential for the production of composite panels as an alternative material to conventional panels such as particleboard and fiberboard In this paper, the utilization of agricultural waste materials as new fiber resources in the manufacture of composites like particleboard and fiberboard and their potential benefits were briefly discussed.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.233
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0690.035

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 head0.027
GPT teacher head0.236
Teacher spread0.208 · 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; both teacher heads agree on what is shown here.

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
Published2002
Admission routes1
Has abstractyes

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