The global Arctic: The sustainability of communities in the context of changing ocean systems : G7 Science Academies' Statement 2018
Bibliographic record
Abstract
This statement was written by the National Academies of Sciences of the G7 states under the lead of the Royal Society of Canada with the purpose of giving scientific advice to the Summit of Heads of State and Governments of the G7 states in Canada in 2018. The Arctic is being profoundly transformed by climate change. This has implications on terrestrial and marine ecosystems, affecting those who live on and from them. It is time to develop a shared scientific vision to protect these vital ecosystems as best we can, produce science for evidence based decision-making and enhance collaborative scientific investigations of these issues. The G7 Academies propose the following: Research cooperation relying on augmented interdisciplinary research supported by large scale international science initiatives in combination with cooperative decision-making among Arctic nations; Training individuals from a diversity of fields and backgrounds, including those residing in the Arctic, to ensure the necessary scientific capacity to address global and local issues; Accessible, usable and timely science databases that can be shared among all stakeholders and decision makers; Programs on remote sensing linked with in-situ monitoring activities integrating sustained highinclination satellite missions, new technologies for underwater measurements and regionally integrated in-situ monitoring that incorporates local knowledge
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.030 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 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".