Bibliographic record
Abstract
2015年10月27-29日にカナダ・ウォータールー大学で，第2回国際極域データフォーラム（Polar Data Forum Ⅱ）が開催された．2013年10月に東京で開催した国際極域データフォーラム（International Forum on “Polar Data Activities in Global Data Systems”）を継続し，両極での地球科学データの蓄積と公開，それらを活用した研究成果とデータの利活用に関する議論の場として，南極科学委員会（SCAR）下の南極データマネジメント委員会（SC-ADM），並びに国際北極科学委員会（IASC）下の北極データ委員会（ADC）の共催により企画された．Polar Data Forum ⅡにはSCAR・IASC 関係者をはじめ，汎地球規模のデータ関連組織の関係者，計109名（15カ国）が参加し，SC-ADM やADC の年次会合を持つとともに，極域データの管理運営に関する今後の指針を検討した． The Second Polar Data Forum (＂Polar Data ForumⅡ＂) was held on October 27-29, 2015 in Waterloo, Ontario, Canada to build on the successes of the first Polar Data Forum held in October 2013 in Tokyo, Japan. Polar Data Forum Ⅱ further refined the themes and priorities regarding polar data management and accelerated progress by establishing clear actions to address the target issues, including meeting the needs of society and science through the promotion of open access data and effective data stewardship, establishing the sharing and interoperability of data at various levels, developing reliable data management systems, and ensuring the long-term preservation of data. The forum was attended by 109 participants from 15 countries, in conjunction with the scheduled annual meetings of the Arctic Data Committee (ADC) of the International Arctic Science Committee (IASC) and the Standing Committee on Antarctic Data Management (SC-ADM) of the Scientific Committee on Antarctic Research (SCAR). Polar Data Forum Ⅱ, together with SC-ADM and ADC annual meetings, proved to be important for showcasing polar data initiatives, for learning from global partners, and working collaboratively to continue developing an international vision and action plan.
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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 | 0.024 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.120 | 0.059 |
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".