Arctic PASSION – Working together towards a better coordinated, integrated, equitable and useful Arctic Observing System: successes, challenges and lessons learnt
Bibliographic record
Abstract
The EU-funded Arctic PASSION project is co-creating an observing system that is better tuned to deliver on the needs of Arctic societies, and that enables a more holistic approach to monitoring of environmental changes to support decision making at local, national and international levels. Arctic Indigenous communities and northern communities are engaged in these activities which involve consideration of high quality, science-based Earth observation information, and of consented Indigenous Knowledge (IK) and Local Knowledge (LK).We will showcase our activities on the co-creation of an Indigenous-led database that documents local bio-cultural events in several Arctic communities, the co-creative establishment of a monitoring system for noise pollution affecting marine mammals with local hunters in Qaanaaq, Greenland, and the initiation of an expert panel of Indigenous and non-Indigenous Arctic people jointly with researchers, and representatives of government agencies and the private sector, to improve coordination of sea ice and related observations by incorporating Indigenous and Local Knowledge and filling gaps in current monitoring systems to provide shared benefit to Arctic communities, and across regions and sectors . In addition we will present our activities to form an Arctic Ocean Regional Alliance (ArORA) under the Global Ocean Observing System (GOOS) framework that will enhance collaboration and coordination of actions dedicated to the Arctic Ocean, bringing together coastal communities, scientists and other Arctic actors.
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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 | 0.035 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".