Canadian Ice Island Drift, Deterioration and Detection database (CI2D3 database)
Bibliographic record
Abstract
Ice islands are massive, tabular icebergs which calve from ice shelves and floating glacier tongues. The ability to identify, monitor and predict the drift and deterioration of these immense ice hazards is crucial for mitigating the associated risks to marine navigation and offshore infrastructure in their vicinity. A joint initiative between the Water and Ice Research Lab (Carleton University) and the Canadian Ice Service (Environment Canada) was established in 2014 to extract pertinent information from available satellite imagery and build a geospatial database for future drift and deterioration analyses, remote-sensing detection and modeling calibration and validation. Implementation of the Canadian Ice Island Drift, Deterioration and Detection database (CI2D3; wirl.carleton.ca/CI2D3) is well-underway, starting with the influx of ice islands through eastern Canadian waters after massive calving events at the Petermann Glacier in 2008 and 2010. Thousands of archived RADARSAT-1 and -2 (Canadian Space Agency/MacDonald Dettweiler and Associates) and Envisat (European Space Agency) synthetic aperture radar images are now being exploited to track ice islands until they are too small to delineate (~<0.25 km2). More than four thousand ice island polygons pertaining to the 2008 and 2010 events have so far been delineated in ArcGIS. The relationship between each ice island and its daughter fragments is captured to permit longitudinal studies.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".