Deliverable 1.10 Roadmap for a sustainable Arctic Observing System
Bibliographic record
Abstract
The INTAROS Roadmap describes the way forward to improve and sustain the observing capacity in the Arctic. The Roadmap addresses the full data delivery chain from observing sensors to data repositories with focus on in situ observations. The document describes key factors determining how well an observing system can function in the Arctic, involving technological advances, infrastructure and data networks. Furthermore, the document emphasize the importance of cross disciplinary collaboration and stakeholder engagement as part of the data delivery chain. The development of in situ observing systems in the Arctic, especially ocean-based observations in the ice-covered regions, depends heavily on mature technology, transport infrastructures and logistical services allowing personnel to access the areas. Deployment and operation of observing platforms require use of icebreakers, aircraft, manned ice stations and automated systems that can operate year-round Based on the experience and knowledge of the INTAROS consortium, the following recommendations are formulated: The importance of in situ observations must be promoted as the backbone for building knowledge about climate and environmental change in the Arctic, at the same level as satellite observations and modelling systems The funding mechanisms for in situ observing systems need to be strengthened and coordinated between programmes, projects and institutions involved in Arctic observation, including local communities The Joint Statement of Ministers (ASM 2021), signed by 25 countries and six Indigenous Peoples organizations, states that they agree to strengthen cooperation on implementing Arctic observing and data sharing, implying that they need to allocate resources for in situ measurements contributing to the observing systems. Technology development for more robust and reliable in situ observing systems is needed. Here, major industry actors can play a role by investing in platforms and sensors that can operate autonomously in the Arctic The data delivery chain from in situ observing systems must be operationalised for each of the discipline-oriented systems in order to facilitate data sharing. This requires collaboration between the research communities, data services and other actors involved in the delivery chain. Collaboration can be enhanced by setting up mediators who can communicate between the actors Observing systems must be adapted to evolving priorities, requirements, and technological developments. This requires regular dialogue with researchers, stakeholders in private and public sector, researchers, service providers, local communities and Indigenous rightholders in the Arctic. Competence building need to be strengthened in observing methods, technologies, and procedures across gender and generations. The INTAROS roadmap builds on the experience and knowledge from the INTAROS consortium comprising more than 300 scientists from 49 institutions in Europe, Asia, and North America. In addition the document builds on discussions with representatives of Indigenous and local communities, private and public stakeholders, scientists and service providers at more than 50 workshops organised by the INTAROS project.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.229 | 0.207 |
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".