Shared Arctic Variable Framework Links Local to Global Observing System Priorities and Requirements
Bibliographic record
Abstract
The geographic settings and interests of diverse groups of rights- and stakeholders figure prominently in the need for internationally coordinated Arctic observing systems. Global and regional observing systems exist to coordinate observations across sectors and national boundaries, leveraging limited resources into widely available observational data and information products. Observing system design and coordination approaches developed for more focused networks at mid- and low latitudes are not necessarily directly applicable in more complex Arctic settings. Requirements for the latter are more demanding because of a greater need for cross-disciplinary and cross-sectoral prioritization and refinement from the local to the pan-Arctic scale, in order to maximize the use of resources in challenging environmental settings. Consideration of Arctic Indigenous Peoples’s observing priorities and needs has emerged as a core tenet of governance and coordination frameworks. We evaluate several different types of observing systems relative to the needs of the Arctic observing community and information users to identify the strengths and weaknesses of each framework. A typology of three approaches emerges from this assessment: “essential variable,” “station model,” and “central question.” We define and assess, against the requirements of Arctic settings, the concept of shared Arctic variables (SAVs) emerging from the Arctic Observing Summit 2020 and prior work by the Sustaining Arctic Observing Networks Road Mapping Task Force. SAVs represent measurable phenomena or processes that are important enough to multiple communities and sectors to make the effort to coordinate observation efforts worthwhile. SAVs align with essential variables as defined, for example, by global observing frameworks, in that they guide coordinated observations across processes that are of interest to multiple sectors. SAVs are responsive to the information needs of Arctic Indigenous Peoples and draw on their capacity to codesign and comanage observing efforts. SAVs are also tailored to accommodate the logistical challenges of Arctic operations and address unique aspects of the Arctic environment, such as the central role of the cryosphere. Specific examples illustrate the flexibility of the SAV framework in reconciling different observational approaches and standards such that the strengths of global and regional observing programs can be adapted to the complex Arctic environment.
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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 | 0.044 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".