Replication Data for "Photo-Crosslinked Diels-Alder and Thiol-ene Polymer Networks"
Bibliographic record
Abstract
The raw data provided is from each of the groups of experiments performed in the manuscript. Raw measurement data was most often converted into Microsoft Excel form for plotting of the data. The naming of the samples was done the same way as the paper and the file names are just: sample name-test name (ex: T2A-DSC). There are 6 folders: 1. Differential Scanning Calorimetry (DSC): 7 Excel files that summarize the thermal transitions of the materials studied. 2. Thermogravimetric Analysis (TGA): 6 Excel files that summarize the measurements for thermal stability 3. Gel permeation chromatography (GPC): 5 Excel files that take the data and convert into the molecular weight distributions of each of the 5 sample polymers studied in the manuscript. 4. Rheology: 3 Excel files that describe the rheological tests conducted on the material (frequency sweeps, dynamic mechanical analysis and tack tests) 5. Proton Nuclear Magnetic Resonance (1H NMR): 5 Excel files + folder with kinetic samples (5 zip files MestReNova) that are used to determine composition and kinetic data (i.e. conversion) from NMR data. 6. Fourier Transform Infra-Red Spectroscopy (FTIR): 22 CSV files that contain each of the FTIR spectra for the various samples.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.127 | 0.101 |
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".