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Record W4319872391 · doi:10.1088/1402-4896/acbb3d

Trajectories of directed lattice paths

2023· article· en· W4319872391 on OpenAlexafffund
E J Janse van Rensburg

Bibliographic record

VenuePhysica Scripta · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicTheoretical and Computational Physics
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAlgorithmLattice (music)PhysicsComputer science

Abstract

fetched live from OpenAlex

Abstract The distribution of monomers along a linear polymer grafted on a hard wall is modelled by determining the probability distribution of occupied vertices of Dyck path and Dyck meander models of adsorbing linear polymers. For example, the probability that a Dyck path passes through the lattice site with coordinates <?CDATA $(\lfloor \epsilon n\rfloor ,\lfloor \delta \sqrt{n}\rfloor )$?> ( ⌊ ϵ n ⌋ , ⌊ δ n ⌋ ) in the square lattice, for 0 < ϵ < 1 and δ ≥ 0, is determined asymptotically as n → ∞ and this uncovers the probability density of vertices along Dyck paths in the limit as the length of the path n approaches infinity: <?CDATA \begin{eqnarray*}{{\mathbb{P}}}^{(D)}(\epsilon ,\delta )=\displaystyle \frac{4{\delta }^{2}}{\sqrt{\pi \,{\epsilon }^{3}{\left(1-\epsilon \right)}^{3}}}\,{e}^{-{\delta }^{2}/\epsilon (1-\epsilon )}.\end{eqnarray*}?> P ( D ) ( ϵ , δ ) = 4 δ 2 π ϵ 3 1 − ϵ 3 e − δ 2 / ϵ ( 1 − ϵ ) . The properties of a polymer coating of a hard wall and the density or distribution of monomers in the coating is relevant in applications such as the stabilisation of a colloid dispersion by a polymer or in a drug delivery system such as a drug-eluding stent covered by a grafted polymer.

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)0.001
metaresearch head score (Gemma)0.004
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 score0.013
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.001

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 head0.012
GPT teacher head0.240
Teacher spread0.228 · 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
Published2023
Admission routes2
Has abstractyes

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