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Record W2999055884 · doi:10.6000/1929-5995.2019.08.10

Thermal Resistance Properties of Polyurethanes and its Composites: A Short Review

2019· review· en· W2999055884 on OpenAlex
Javier Carlos Quagliano Amado

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.

venuePublished in a venue whose home country is Canada.
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

VenueJournal of Research Updates in Polymer Science · 2019
Typereview
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials sciencePolyurethanePetrochemicalIsocyanateComposite materialThermal resistancePolyolMicrostructureChemical resistanceExtenderThermalOrganic chemistry

Abstract

fetched live from OpenAlex

The nature of starting materials and the conditions of polyurethane (PU) preparation are regarded as the main general parameters that determine PU thermal resistance. The effect of structure and presence of additives were identified as the major general factors on this regard. Structural factors include phase microstructure, i.e. chemical structure, proportion and segregation of soft and hard segments); polyol type (petrochemical or natural oil-based); isocyanate and chain extender type and thermoplasticity of PU. Respect to the effect of additives, the incorporation of fillers is the most direct strategy to increase PU heat resistance. With respect to fiber additives, in general a positive effect is found on improving thermal resistance, although this generalization could not apply, considering the large number of different PU and environmental conditions of usage.

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)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.289
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0030.001
Research integrity0.0000.001
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 head0.146
GPT teacher head0.418
Teacher spread0.273 · 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