Novel Therapeutic Strategies for Tissue Engineering of Bone and Cartilage Using Second Generation Biomimetic Scaffolds (EXPERTISSUES)
Bibliographic record
Abstract
The main aim of the proposed network of excellence (Noël) is to combat and overcome fragmentation of European Research on the field of Tissue Engineering of Bone and Cartilage. The network will bring together Europe's leading academic centres and several complementary industrial players in a multi-disciplinary consortium to conduct and structure research that is able to compete in the internationally arena, namely with USA and Japan. The constitution of this network of excellence will lead to a complete restructuring and reshaping of the European research in this field. The size of the network (20 partners from 13 countries, including 9 of the EU member states), and the selection of its original members, was designed in order to join together the critical mass and all the expertises needed to be an unavoidable world reference on the topic of tissue engineering of bone and cartilage. In order to achieve that, the network also incorporates, as part of an International Advisory Board (not funded by EU), academic (but not industrial) partners of leading institutions in the USA, Canada and Singapore. These partners, leaded in most cases by researchers of EU nationality, agreed to join the network bringing in a specific expertise that will help to move European research on that particular topic. This Noël aims to provide new tissue engineering technologies for therapeutic treatments, which will ultimately have a major social impact by contributing to the challenge of providing lifelong health for our society at an affordable cost.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".