From Knowledge to Solutions: Science, Technology and Innovation in Support of the UN SDGs
Bibliographic record
Abstract
This white paper represents the collective perspectives of a network of legal entities based in Europe and with global interests, which includes biodiversity, ecology, and engineering communities, aiming to strengthen Science, Technology, and Innovation (STI) efforts toward achieving the United Nations (UN) Sustainable Development Goals (SDGs). With their combined expertise and through European initiatives such as the Research Infrastructures, the e-Infrastructures, the European Open Science Cloud (EOSC), the Digital Twin projects and academic publishers, these communities provide a base for collaboration in strategically contributing to the implementation of the Kunming-Montreal Global Biodiversity Framework targets. Furthermore, these communities seek to forge an international alliance to further integrate biodiversity conservation into the UN Summit of the Future priorities and the post-SDG agenda.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.021 | 0.014 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".