Ya empieza a vislumbrarse la nube
Bibliographic record
Abstract
En 2015, la Comisión Europea tuvo la idea de construir una infraestructura federada para apoyar la ciencia en abierto y poder desarrollarla, priorizando el intercambio de datos surgidos de la actividad investigadora y la generación de servicios a partir de estos datos. Esta infraestructura recibió el nombre de Nube Europea para la Ciencia en Abierto (European Open Science Cloud o EOSC). La Comisión constituyó un primer grupo de expertos que elaboró el documento Realising the European Open Science Cloud, donde se proponían una serie de recomendaciones para crear esta nube y después ha ido financiando diferentes proyectos como el EOSCPilot o el EOSCHub hasta llegar al viernes 23 de noviembre cuando se presentó la primera versión de la infraestructura, el portal de la EOSC...
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".