MétaCan
Menu
Back to cohort
Record W4302119290 · doi:10.9734/csij/2017/33377

Phenolic Foam Reinforced by Cedar's Resin

2017· article· en· W4302119290 on OpenAlexaff
Ramzy Hamed, Yolla Kazzi, Houssein Awada

Bibliographic record

VenueZenodo (CERN European Organization for Nuclear Research) · 2017
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsCegep de Victoriaville
Fundersnot available
KeywordsComposite materialMaterials science

Abstract

fetched live from OpenAlex

<p>Recently phenolic foams have been received much attention because of their excellent properties including flame resistance, low density, high thermal stability over a broad temperature range and low generation of toxic gases during combustion. Cedar resin was used as a toughening agent to modify the brittleness of phenolic foam. The cedar resin was first introduced to the phenol formaldehyde resin. The mixture was successfully used to prepare phenolic foam using appropriate combinations of flowing agent. Benzene Sulfonic acid was employed as a curing agent. Orthophosphoric acid and nonionic surfactant polyoxyethylene were used as foaming agent and surfactant respectively.</p>\n\n<p>The mechanical properties results showed that the incorporation of cedar resin into phenolic foam dramatically improved the compressive strength indicating the excellent toughening effect of cedar resin. In addition this property is depended to the percentage of the cedar resin. The apparent density data indicated that the addition of cedar resin can increase the apparent density of phenolic foam.</p>

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.000
Scholarly communication0.0020.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.010

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 head0.031
GPT teacher head0.255
Teacher spread0.224 · 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 designNot applicable
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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueZenodo (CERN European Organization for Nuclear Research)Same topicPolymer composites and self-healingFrench-language works237,207