Path-Space Ratio as a Molecular Shape Descriptor of Polymer Conformation
Bibliographic record
Abstract
Polymers at interfaces exhibit properties that cannot be completely captured by descriptors of mean molecular size. Recent work in the literature shows that a combined analysis of mean size and chain entanglement provides a more discriminating approach to understanding the onset of configurational transitions in these systems. Usually, chain entanglement is characterized by properties such as the mean overcrossing number or the chain's writhe; these are powerful properties but their evaluation can be computationally demanding. In this work, we introduce a geometrical descriptor of polymer shape, termed the path-space ratio zeta, aimed at quantifying essential features of chain complexity, but at a lower computational cost. The descriptor includes information on chain geometry and topology. The path-space ratio zeta is built by taking into account two key ideas: (a) a dimensionless measure of length along the backbone of the polymer, and (b) the behavior of topological "knot energies". Here, we compare zeta with other approaches to quantify polymer geometry and connectivity. Particular attention is devoted to the ability of these descriptors to discriminate and quantify conformational changes in grafted polymers under compression. We show that, for these types of applications, the path-space ratio presents a fast alternative to the mean overcrossing number.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".