AGINFRA PLUS D8.3 - AGINFRA Future Science Recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This document serves as a white paper, which describes the AGINFRA PLUS vision of a next-generation community driven web-based research infrastructure, as well as present and future application scenarios that can be envisaged from experiences with the piloted AGINFRA PLUS user communities and provides recommendations that aim to inform the roadmap for developing AGINFRA PLUS infrastructure further. The initial version of this white paper has been prepared with contributions from all partners and was submitted to the EC on M25. In order to further enhance the positioning of the project in the digital science ecosystem, Agroknow worked on a next version of this deliverable that has been organised as an edited volume. A variety of stakeholders, including all project partners, were invited to contribute to this volume titled “Digital Science Recommendations for Food & Agriculture”. External contributors included strategic digital infrastructure initiatives (such as OpenAIRE, the FNH-RI Research Infrastructure for Food,<br> Nutrition and Health, and the METROFOOD Research Infrastructure for promoting metrology in food and nutrition), as well as international stakeholders (such as the University of Guelph, Canada; and the Chinese Academy of Agricultural Sciences). The full text of the volume can be found in Annex A of the present document.
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.028 | 0.016 |
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 it