Arctic risk: a discussion of the possible outcomes of two disaster scenarios
Bibliographic record
Abstract
We believe that the risks associated with Arctic development often involve the overlap of disciplines \nand sectors. Based on this belief, we organized an Arctic Risk Scenario meeting on the 12th September \n2014. Invited participants worked through two Arctic disaster scenarios. The scenarios chosen were: \n(1) a cruise ship sinking off north east Spitzbergen, and (2) an oil well blowout in the Kara sea. \nParticipants came from the oil and gas industry, shipping, law, politics, humanitarian agencies and \nacademia: one aim of the meeting was to bring together diverse perspectives on the Arctic. \nTwo invited speakers presented relevant background. Dr Nataly Marchenko (The University Centre in \nSvalbard (UNIS), and author of the book “Russian Arctic Seas”) discussed a series of recent Arctic \nshipping near8disasters. Dr Rocky Taylor (C8Core, St John’s, Newfoundland, Canada) discussed the \ncomplexities of oil exploration and production in Arctic seas, again based on a number of reference \nevents. The rest of the meeting was held under the Chatham House Rule. Diverse views were \nexpressed, and in this document we highlight topics of broad consensus and areas of disagreement. \nThis document highlights observations and outcomes from the meeting which may affect the UK’s \nongoing interests in Arctic development. It was submitted to, and published by, the UK House of Lords \nArctic Committee, under their 2014 call for evidence.
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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 | 0.021 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.014 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.022 | 0.014 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".