Deliverable 6.6 Towards a pan-AOSS: status of cooperation with non-EU partners and organizations
Bibliographic record
Abstract
In this report we analyze the state of cooperation with non-EU partners in relation to the general objective of the Arctic PASSION project to implement a pan-Arctic Observing System of Systems (pan-AOSS). Among non EU-partners, great attention in the report is devoted to Asian partners of countries that sit as observers at the Arctic Council, considering that, in participating in this common effort, they have limitations and experience challenges similar to those faced by partners from non-Arctic European countries. Since Singapore's interest and contribution to scientific research until now has been marginal, analysis involves only 4 of the 5 Asian states sitting as observer in the Arctic Council: India, Korea, China, and Japan. History of the commitment in the Arctic of those countries is briefly reviewed, and information for assessing the status and dynamics of the contribution to long-term observation of the Arctic are also provided. These elements are the basis to discuss the level of bilateral and multilateral cooperation, and the natural continuation of such commitments over the years. The most useful platforms existing at national, regional and multilateral levels to develop the discussion on pan-AOSS design and implementation are listed and briefly described. About North American partners, strategic partners in a pan-AOSS perspective are identified by an analysis of the dynamics that currently characterize the American and Canadian effort for long-term observation of the Arctic. The Report focuses on subjects that can ensure/support long-term commitments. The importance of coordination platforms recently developed, as AsFF, and projects like Arctic PASSION and RNA CoObs underlined. The picture of the current state of cooperation and coordination, and their potential, is completed by considering the developments of last years, regarding the creation of a pan-Arctic alliance for the marine domain (ArORA). We provide a brief description of the work carried out by that initiative, and identify and describe the platforms we could use to give concreteness to the implementation of pan-AOSS in the necessary long-term perspective. The elements collected in this way allow us to make some considerations and identify several topics/areas on which the effort could be focused in the near future
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.132 | 0.121 |
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".