Arctic Observing Summit (AOS) 2018 Statement and Call to Action
Bibliographic record
Abstract
he Arctic observinG summit (AOS) is an international conference that aims to provide community-driven, science-based guidance for the design, implementation, coordination, and operation of a sustained (decades-long) international network of Arctic observing systems.The AOS provides a platform for addressing urgent and broadly recognized needs of Arctic observing across all components of the Arctic system, including the human component.The AOS fosters communication and international collaboration and coordination of long-term observations to improve our understanding of system-scale Arctic change and responses to it.The AOS is an international forum that aims to optimize resource allocation, minimize information gaps, and avoid duplication by coordinating efforts and exchanging information among researchers, agencies, northern peoples, non-governmental organizations, the private sector, and others involved or interested in longterm observing activities.The 2018 AOS was the fourth Summit, following those held in Vancouver, Canada, in 2013, Helsinki, Finland in 2014, and Fairbanks, USA, in 2016.Summits are structured thematically, building iteratively on previous outcomes and recommendations.In 2018, delegates focused on the business case for a pan-Arctic observing system.The following statement summarizes the main conclusions, recommendations, and call to action from the Arctic Observing Summit 2018 held in Davos, Switzerland, on 24-26 June 2018.The Summit Statement is based on the summaries of the working group deliberations and was reviewed during the final plenary session of the conference.In addition to the Summit Statement below, background documents, presentations, and materials from previous events can be found at www.arcticobservingsummit.org.
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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.027 | 0.015 |
| Insufficient payload (model declined to judge) | 0.020 | 0.016 |
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".