Tumor-Specific Blood Serum Factors as Basis of Tumor Dormancy
Bibliographic record
Abstract
Environmental pollution, climate change and biodiversity loss are major health threats identified by the EU Action Plan “Towards Zero Pollution for Air, Water and Soil”. Addressing these challenges requires highly qualified toxicologists and ecotoxicologists, capable of evaluating emerging pollutants, complex mixtures and associated risks using innovative methods. The Erasmus+ Project ToxLearn4EU (2021-1-FR01-KA220-HED-000030081; https://toxlearn4eu.eu) was launched to modernise higher education in toxicology across Europe by designing and implementing open-access digital resources. The project brings together ten higher education and research institutions from eight European countries. It focuses on three key areas: emerging pollutants, new toxicological methodologies and models, and risk assessment and communication. ToxLearn4EU has produced a series of online training materials, including core and advanced courses, case studies, and expert lectures—available via YouTube and the project website. These resources target both students and teachers, aiming to enhance competencies, promote digital literacy, and support curriculum renewal in line with the European Green Deal. The pedagogical approach combines interactive learning and real-world applicability, promoting engagement through problem-based learning and thematic integration. Teachers are also supported with implementation guidelines to incorporate these resources into regular teaching practices. The courses contribute to life-long learning and academic-professional transitions in human and environmental toxicology. By aligning digital innovation with sustainability goals, ToxLearn4EU strengthens toxicological education across Europe, improves student motivation and reduces dropout rates, and reinforces cross-border collaboration in science education. Acknowledgements: The authors acknowledge the contributions of all ToxLearn4EU partners. Funding: Funded by Erasmus+ KA220-HED - Cooperation Partnerships in Higher Education.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".