D2.3. Report on potential science - industry priorities in research and observations
Bibliographic record
Abstract
In the context of the global climate change, where the Arctic sea ice has been shrinking with acceler-ating losses in the last two decades starting to make commercially viable sea routes through the Arctic, the ARICE project aims at establishing an international cooperation strategy to better coordi-nate the existing polar research fleet, to offer transnational access to a set of international High Arctic research icebreakers, and to collaborate with maritime industry in a “programme of ships and plat-forms of opportunity”. The achievement of these goals represents a fundamental step to provide information on the state of the Arctic Ocean that is urgently needed due to the fast increase of the Arctic marine traffic. Safe navigation and voyage planning in Arctic waters as well as sustainability in operations, in particular concerning environmental aspects related to shipwrecks, oil spill risk, ship-ping impacts, underwater noise, invasive species, require improved weather and sea ice forecast, that has to be supported by investments in hydrographic, meteorological and oceanographic data. In particular, safe navigation requires additional hydrographic surveys to improve Arctic navigation charts, and systems to support realtime acquisition, analysis and transfer of meteorological, ocean-ographic, sea ice and iceberg information. Results in this direction can be achieved only through international cooperation not limited to the scientific community but extended to industry involved in Arctic exploitation and services or in some way impacted by Arctic climate changes. With the awareness that “science-stakeholder connection follows an iterative process: iteration ensures better adjustment of the research priorities to the so-ciety expectations”1, the ARICE project, starting from previous activities carried out by the Interna-tional Arctic Science Committee (IASC) and EU-PolarNet project, promoted a path of interactions be-tween the scientific community and industrial stakeholders in the Arctic which allowed to identify in a first phase common themes of research, innovation and technological development, and specific industrial research interests in a second phase. Furthermore, the discussion highlighted the absence of instruments capable of carrying out automatic measurements in the field of physic-chemical quan-tities of significant scientific interest, opening the way for the development of new products by high-tech companies. This activity was mainly supported by the organization of a research-industry session at Arctic Circle Assembly 2019 in cooperation with EU polar cluster members and a side event Workshop at the Sustainable Ocean Summit 2019. This report is organized as it follows. Previous activities aiming at connecting science and industrial stakeholders are summarized and discussed in section 2. Section 3 reports ARICE workshop activities and contributions with an overall discussion of their results identifying potential science-industry pri-orities in research and observation. Major cooperation, industrial and science needs are reported. Concluding remarks will summarise open issues and possible future steps.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.051 | 0.043 |
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".