D9.1 - 1 st REPORT ON GOOD PRACTICES ON TRAINING & LEARNING EXCHANGE PROGRAM (BCC ACTIVITIES, SUMMER SCHOOLS, BLUE MOVE ACTIVITY, REPORT OF ACTIVITES FROM THE STUDENTS' EXPERIENCE)
Bibliographic record
Abstract
The deliverable reports on the achievements were accomplished within WP9 during the first year of the project. “Virtual Blue Career Center in the Black Sea”, has been launched to provide a platform for blue economy-related professional courses, qualifications, mobility and employment opportunities, projects, internships, workshops, and all information related to the Blue Move subtask for young students and scientists. Under Ph.D. “Programme on Blue Growth”, Ph.D. calls are launched. The “Summer Schools & Training and Learning Exchange Program” was conducted successfully on 23-25 August 2022 with the participation of 12 early career ocean professionals from Black Sea countries. During the summer school, leading researchers and blue economy experts across Europe shared the fundamentals of marine science and sustainable blue economy in the Black Sea. Blue Move” Activity, led by CETMAR, delivered Good practices on Blue Move. The Black Sea Young Ambassadors Programme launched a call and selected the second cohort of Young Ambassadors. Jointly with the Black Sea CONNECT Project, Black Sea Young Ambassadors conducted local, regional and online activities (such as field trips, photography contests, informative workshops, and social media posts including fact sheets and videos ) to raise awareness on the Black Sea and the Strategic Research and Innovation Agenda during Spring-Summer 2022 period. Lastly, Hands-on activities for school children started with a selection of teaching material for schools and the identification of schools to work with.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.121 | 0.073 |
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".