CINECA: Report on trust model for partner sites, and between sites and controlled-access researchers
Bibliographic record
Abstract
In this deliverable document, we report on the activities for Deliverable 4.1 - Report on trust model for partner sites, and between sites and controlled-access researchers. The Work Package 4 goals concern the development of a set of tools that can facilitate federated analyses of new and diverse genetic and genomic datasets, based on specific use cases. The tools selected will be using the common federated infrastructure established in earlier work packages, and the datasets will be described with metadata standards identified in Work Package 3. <br> In our report we considered trust as the extent to which one party is willing to depend on the other party in a given situation with a feeling of relative security, even though negative consequences are possible. This work has contributed towards establishing a description of the trust model and four different levels of data access concerning specific cohort’s data, identifying use cases for the development of federated analysis workflows and describing existing data access models to inspire subsequent WP4 deliverables related to the implementation of the federated analysis workflow.
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 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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.004 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".