Sustaining Arctic Observing Networks’ (SAON) Roadmap for Arctic Observing and Data Systems (ROADS)
Bibliographic record
Abstract
Arctic observing and data systems have been widely recognized as critical infrastructures to support decision making and understanding across sectors in the Arctic and globally. Yet due to broad and persistent issues related to coordination, deployment infrastructure and technology gaps, the Arctic remains among the most poorly observed regions on the planet from the standpoint of conventional observing systems. Sustaining Arctic Observing Networks (SAON) was initiated in 2011 to address the persistent shortcomings in the coordination of Arctic observations that are maintained by its many national and organizational partners. SAON set forth a bold vision in its 2018 – 28 strategic plan to develop a roadmap for Arctic observing and data systems (ROADS) to specifically address a key gap in coordination efforts—the current lack of a systematic planning mechanism to develop and link observing and data system requirements and implementation strategies in the Arctic region. This coordination gap has hampered partnership development and investments toward improved observing and data systems. ROADS seeks to address this shortcoming through generating a systems-level view of observing requirements and implementation strategies across SAON’s many partners through its roadmap. A critical success factor for ROADS is equitable participation of Arctic Indigenous Peoples in the design and development process, starting at the process design stage to build needed equity. ROADS is both a comprehensive concept, building from a societal benefit assessment approach, and one that can proceed step-wise so that the most imperative Arctic observations—here described as shared Arctic variables (SAVs)—can be rapidly improved. SAVs will be identified through rigorous assessment at the beginning of the ROADS process, with an emphasis in that assessment on increasing shared benefit of proposed system improvements across a range of partnerships from local to global scales. The success of the ROADS process will ultimately be measured by the realization of concrete investments in and well-structured partnerships for the improved sustainment of Arctic observing and data systems in support of societal benefit.
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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 | 0.015 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.009 |
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".