Editorial: Bio and nanomaterials in tissue engineering and regenerative medicine (BioNTERM)
Bibliographic record
Abstract
The collection of articles includes studies on translational materials such as a peptide-based material to on-the-spot cornea repair (10.3389/fbioe.2021.773294) and the impact of electron-bean irradiation on pre-made collagen-based corneal implants (10.3389/fbioe.2022.883977). Fundamental research on the effects of processing techniques for obtaining silk-fibroin (10.3389/fbioe.2021.777320) and using plant viral nanoparticles as an additive for gelatin methacryloyl hydrogels for building complex 3D structures (10.3389/fbioe.2022.907601). This special issue includes two mini-reviews that revise recent developments on using peptides to prepare biomaterials (10.3389/fbioe.2022.893936) and some of the most common methodologies used in bioengineering lung scaffolds (10.3389/fbioe.2022.1011800). Finally, a comprehensive review of the use of mRNA-containing biomaterials for bone repair is also part of this special issue (10.3389/fbioe.2022.952670).The editors of this special issue would like to thank the scientists who contributed their work to this volume. While we are not still over the COVID-19 pandemic, we are convinced our scientific community has become stronger, is more resilient, and highly connected. The lessons learned on using digital technologies to connect, network, and share scientific knowledge during the pandemic are crucial to building a more inclusive scientific and societal ecosystem for future generations.The editors
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.005 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.034 | 0.026 |
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".