Report on a workshop of the working group on Atmosphere-Related Research in Canadian Universities
Bibliographic record
Abstract
Over the past year a working group of researchers based in Canadian universities have engaged in a strategic planning activity intended to identify and articulate academic research and education priorities in atmospheric, ocean, climate, and related research in the coming five to seven years.The areas of research thus identified have been tentatively grouped under the name "Atmosphere-Related Research" (ARR).The activity stemmed from discussions about changes in funding and partnerships between university and government researchers.The activity was stimulated and focused by a workshop at McGill University in August 2014, that was hosted by the US University Corporation for Atmospheric Research (UCAR), where new ideas on how to move forward with organizing this Canadian community were considered.The initial aim of the "Atmospheric Related Research in Canadian Universities" (ARRCU) working group is to produce a short White Paper that will serve as the basis for future strategic planning and organizational activities.(The organizing committee of the ARRCU Working Group are the authors of this report.)On April 23, 2015, a draft version of this White Paper was circulated and on May 8, 2015, a workshop was held to discuss the draft White Paper and other aspects of this initiative.The purpose of this report is to summarize the proceedings of the workshop.Workshop materials, including background documents, slide decks, audio recordings and session summaries are available at http://tinyurl.com/arrcu-may2015-workshop.
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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.022 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".