VITALISE D8.2 Final report on Transnational Access 1
Bibliographic record
Abstract
This document aims to present an overview of the Transnational Access (TA) activities conducted in Work Package 8 (WP8) ΤΑ1 - TA to Living Lab infrastructures for rehabilitation as part of the VITALISE project. This final report encompasses six (6) research infrastructures that participated in the context of this work package, presenting information for completed TA activities. In particular, the report includes comprehensive information for each of the participating research infrastructures. It provides a summary of completed TA activities of every research project, the activities performed, the outcomes achieved, and any notable findings or advancements made. In addition, the report for the completed TA activities includes also some information regarding the effort and hours spent from the several professionals of the LL personnel for supporting the execution of each TA activity,both in person and remotely, as well as a summary of the researchers’ TA evaluation and potential opportunities for future collaboration and common publications. This information showcases the value and impact of the TA program on the overall project objectives. As it is the final report, this document offers a snapshot of the collaborative efforts and achievements within WP8's TA1. It signifies the project's progress towards fostering innovation, knowledge exchange, and interdisciplinary research in the realm of rehabilitation and collect all the information generated in the different TA activities 1.
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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 | 0.020 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.098 | 0.049 |
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".