Scripts for "Controlled Tough Bioadhesion Mediated by Ultrasound"
Bibliographic record
Abstract
The matlab codes are used to compute the theoretical acoustic pressure field, which is used in Figs 3D, 3E, 3F, S16 and S17 of the following journal article: Title: Controlled Tough Bioadhesion Mediated by Ultrasound Authors: Zhenwei Ma1, Claire Bourquard2, Qiman Gao3, Shuaibing Jiang1, Tristan De Iure-Grimmel4, Ran Huo1, Xuan Li1, Zixin He1, Zhen Yang1, Galen Yang5, Yixiang Wang6, Edmond Lam5,7, Zu-Hua Gao8, Outi Supponen2*, Jianyu Li1,9* Affiliations: Department of Mechanical Engineering, McGill University; Montréal, QC H3A 0C3, Canada. Institute of Fluid Dynamics, Department of Mechanical and Process Engineering, ETH Zürich; Sonneggstrasse 3, 8092 Zürich, Switzerland. Faculty of Dentistry, McGill University; Montréal, QC H3A 1G1, Canada Department of Bioengineering, McGill University; Montréal, QC H3A 0E9, Canada Department of Chemistry, McGill University; Montréal, QC H3A 0B8, Canada Department of Food Science and Agricultural Chemistry, McGill University; Sainte-Anne-De-Bellevue, QC H9X 3V9, Canada Aquatic and Crop Resource Development Research Centre, National Research Council of Canada; Montréal, QC H4P 2R2, Canada Department of Pathology and Laboratory Medicine, University of British Columbia; Vancouver, BC V6T 1Z7, Canada Department of Biomedical Engineering, McGill University; Montréal, QC H3A 2B4, Canada *Corresponding authors. Emails: jianyu.li@mcgill.ca; outis@ethz.ch
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.325 | 0.086 |
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".