Deliverable 1.1 New possibilities in research in the Arctic Ocean, outcome of workshop with Research Infrastructures.
Bibliographic record
Abstract
This report describes a summary of existing and planned research infrastructures in the central ArcticOcean, as presented and discussed at the HiAOOS-organized Research Infrastructures workshop held inTromsø, Norway, 24-26 January 2024. Concepts and requirements for a pan-Arctic multipurposemooring network for ice-ocean observations, tomography, and geo-positioning were discussed along withpossibilities for future collaboration. Enhanced collaboration with the Argo float communities wasrecommended. Collaboration has been established with the Polar Connect community, working withplans for a trans-Arctic communication cable that can also be used for research. The workshop was organized in sessions with the following themes: 1) fixed research installations, 2)moving platforms and 3) technologies, methods, tools, and data delivery chains. This report follows thesame structure, followed by a brief section with emerging new collaborations and suggestions foradditional assets (moorings, buoys, and floats) that will fill identified gaps in the Arctic OceanObservation System. In total 31 registered and participated in the Workshop. The participants came from Europe, NorthAmerica, and Asia.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.285 | 0.199 |
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".