Bibliographic record
Abstract
addressed essential experimental and theoretical techniques for assessing the structural and dynamic properties of this unique class of materials at a spatial-temporal level.The hybrid conference format involved sessions both on-site and online, providing attendees with insights into the dynamic materials, crystals, and phenomena.The program consisted of a series of keynote and invited talks, contributing presentations, and a poster session that covered a wide range of related topics (Fig. 3).The first day of the conference began with a keynote address by Stephen Loeb (University of Windsor, Canada) on designing mechanically interlocked molecules to function in the solid state, which provided a historical perspective on solid-state dynamics.The focus was on macrocyclic ring rotation, large amplitude translation, molecular switching, and the precise placement and interaction between components with different dynamics.This was followed by an invited talk by Angiolina Comotti (University of Milano-Bicocca, Italy) on rotor dynamics and light-driven motors in 3D porous architectures.In the afternoon, invited lectures covered topics such as pressure-driven phase transitions for solidstate refrigeration by Claire Hobday (University of Edinburgh, UK), non-crystallinity and disorder in dynamic metal-organic frameworks by Sebastian Henke (Technische Universität Dortmund, Germany), and static and dynamic conformational freedom by Stefano Canossa (Max Planck Institute for Solid-
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.067 | 0.014 |
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".