Glacial Ice Hazards Working Group (GIHWG) Report of the Inaugural Meeting 25 & 26 June 2015, at Carleton University
Bibliographic record
Abstract
This report documents the first meeting of the Glacial Ice Hazards Working Group (GIHWG) on the 25 th and 26 th of June, 2015 at Carleton University in Ottawa, Canada.The vision of the group is to bring together the many diverse and widely distributed people concerned with floating hazards composed of freshwater ice (or substantially freshwater ice) every two years.Our purpose is to foster collaboration, data sharing and interdisciplinary work on icebergs and ice islands, and to begin framing and prioritizing research and development objectives where needs are greatest.The origin of the group is based on one of the recommendations from a report entitled Preliminary Research Plan for Glacial Ice Hazards (Saper, 2011), which called for a biannual meeting convened by the Applied Science Group of the Canadian Ice Service (CIS).This report was a deliverable to the CIS under a contract managed by Tom Carrieres.Later, in 2014, Adrienne Tivy (CIS) and Ron Saper (Carleton University) decided to organize such a meeting, but rather than being sponsored by CIS, the meeting would be organized by a committee of individuals from various organizations.Derek Mueller of Carleton University agreed to host the initial meeting, and he joined the organizing committee along with Greg Crocker (Ballicater), Angela Cheng (CIS) and Hai Tran (CIS).Forty-six people participated by attending at least part of the meeting in person, by telephone, by videoconference, or by providing slides in advance of the meeting.The participants included invited academics, government employees, and private sector people that were known to the organizing committee, or who were identified by other invitees.There was no publicity for this initial meeting, no fee for attendance, and no formal papers presented.This report outlines the meeting format, and provides brief summaries of the discussions in a more or less uniform format for readability.For completeness, the detailed breakout summaries prepared by breakout session chairs are included (with some editing) as an Annex to this report.Although there was no fee for participation, all participants were required to contribute, in advance, a seven slide presentation deck adhering to a common template with no distribution restrictions.Virtually all participants complied, and these presentations are a substantial output of the meeting in their own right because they reflect first-hand initial assessments of research priorities, report on many activities, and identify key resources.These presentations are available for download at the working group web page (http://wirl.carleton.ca/gihwg).The meeting identified four broad needs: 1) the need for sharing and archiving of comprehensive data sets/observations; 2) the need for better schemes for model inter-comparison and evaluation; 3) the need to incorporate understanding of climate change and glaciology into activities; and 4) the need to prepare protocols for large scale field operations (response to calving of ice islands).People interested in participating in or keeping current with the activities of the GIHWG should visit the website and follow instructions to subscribe to the email listserv, which is hosted free of charge by Carleton University.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.027 |
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".