BrightSpace Project Newsletter / Edition 2 / December 2023
Bibliographic record
Abstract
BrightSpace Project Newsletter / Edition 2 / December 2023 From this issue of our newsletter, you can get to know: What are the latest developments in our project? Read the letter from our Coordinator, Marc Müller looking back at the 1st year of project implementation From kick-off to our 2nd General Assembly: learn more about how our partners work to keep focus on project implementation. Where and how are we networking and cooperate with other Horizon Europe Projects? Watch our video interview on how we cooperate with our fellow project LAMASUS Learn about the EAAE pre-congress workshops jointly organized with LAMASUS and with GenBEcon and RATION. Read further highlights from our fellow projects, including results from MIND STEP. What are the upcoming international events where you can meet us? There are several events on our list, e.g. BrightSpace will be represented at the EU-Canada Conference on Sustainable Agriculture and the Agricultural Outlook Conference in Brussels organized in the framework of the EU Agri-Food Days between 5 – 8 December 2023. Further information BrightSpace is an EC funded 5-year Horizon Europe research and innovation action aiming to design effective and sustainable strategies to navigate EU agriculture within a Safe and Just Operating Space. BrightSpace Project coordination: Wageningen Economic Research, The Hague, NLContact: brightspace.wecr@wur.nl | Website: www.brightspace-project.eu Project duration: 1 November 2022 – 31 October 2027 Funded by the European Union. Horizon Europe Grant Agreement No. 101060075. Views and opinions expressed are however those of the authors only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them. UKRI Project code: 10047415
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.689 | 0.614 |
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".