Evaluating climate change effects on ice conditions in the lower St. Lawrence River
Bibliographic record
Abstract
Ice thicknesses for the lower St. Lawrence River are presented to support designing/adapting infrastructure for future ice conditions. Various approaches are used to predict ice thicknesses out to the year 2100 under two warming scenarios (RCP4.5 and RCP8.5). Each approach varies in its complexity. Ranked in order of decreasing accuracy, the four approaches are: the NRC model, FDD approach, FLake model, and extrapolation of measurements. The extrapolation approach is only used to put context around our other predictions, not to advocate for it. Three of the approaches require input from climate data (i.e. ERA5 and RCP), which are validated with on-ice measurements and weather station data. Only shorefast ice (ice attached to the shore) is discussed in this report. Ice thickness predictions for the historic period are given for nine sites, three of which are used to provide estimates for the mid-century (2040 to 2060) and latecentury (2080 to 2100) periods. A comprehensive array of estimates is given to document long-term trends, period-averages, percentiles and extreme ice thicknesses.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".