Report of the EuroMAB Conference 2017, Building a sustainable future together, Dordogne Basin Biosphere Reserve, Sarlat-la-Canéda, (France), 4–7 April 2017
Bibliographic record
Abstract
The EuroMAB Network includes all European and North-American member states which participate in UNESCO´s Man and the Biosphere (MAB) Programme and its World Network of Biosphere Reserves (WNBR). EuroMAB is the largest and oldest of the nine regional and inter-regional MAB Networks. It is made up of 36 countries, including Canada and the USA, and includes 302 biosphere reserves (BRs). Bringing together almost half of the total WNBR sites, the EuroMAB Network is a platform for sharing knowledge, know-how and experience of sustainable development, as well as a collective tool for supporting sustainable development practices among BR coordinators, scientists and MAB National Committees. Since the first meeting in 1986 in České Budějovice, Czech Republic, the MAB National Committees and BR coordinators of EuroMAB have met nearly every two years. EuroMAB conferences have been held in Minsk, Belarus (1997), Cambridge, UK (2000), Rome, Italy (2002), Hernstein, Austria (2005), Antalya, Turkey (2007), Stará Lesná, Slovakia (2009), Lundsbrunn, Sweden (2011), Brockeville, Canada (2013), and Haapsalu, Estonia (2015). EuroMAB meetings are dedicated to bringing together BR managers, coordinators, scientists and representatives of UNESCO´s MAB Secretariat, MAB National Committees and partner institutions / associations.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.098 | 0.033 |
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".