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Record W4304607138 · doi:10.1088/1751-8121/ac9936

Thermodynamic and topological properties of copolymer rings with a segregation/mixing transition

2022· article· en· W4304607138 on OpenAlexafffund
E J Janse van Rensburg, Enzo Orlandini, Maria Carla Tesi, S G Whittington

Bibliographic record

VenueArchivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTheoretical and Computational Physics
Canadian institutionsUniversity of TorontoYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCritical point (mathematics)PolymerPhase transitionLattice (music)Space (punctuation)Ring (chemistry)Statistical physicsMetric (unit)ThermodynamicsMetric spacePhase (matter)Materials scienceTopology (electrical circuits)PhysicsPure mathematicsMathematicsChemistryQuantum mechanicsGeometryCombinatoricsComputer science

Abstract

fetched live from OpenAlex

Two ring polymers close to each other in space may be either in a segregated phase if there is a strong repulsion between monomers in the polymers, or intermingle in a mixed phase if there is a strong attractive force between the monomers. These phases are separated by a critical point which has a ✓-point character. The metric and topological properties of the ring polymers depend on the phase, and may change abruptly at the critical point. In this paper we examine the thermodynamics and linking of two ring polymers close in space in both the segregated and mixed phases using a cubic lattice model of two polygons interacting with each other. Our results show that the probability of linking is low in the segregated phase, but that it increases through the critical point as the model is taken into the mixed phase. We also examine the metric and thermodynamic properties of the model, with focus on how the averaged measures of topological complexity are related to these properties.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.187
Teacher spread0.179 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations1
Published2022
Admission routes2
Has abstractyes

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