Using an operational index to foresee future ice scenarios in the Upper St. Lawrence River
Bibliographic record
Abstract
The upper St. Lawrence River between Kingston, at the mouth of Lake Ontario, and Montreal, about 250 km downstream, is an important river reach, with hundreds of infrastructure assets along its shorelines. Hydroelectric dam facilities regulate the flow along that reach. In the winter, water discharge is carefully managed to promote ice coverage and limit backwater extent, and is guided by an operational ice status index (ISI). This paper describes a study to anticipate ice season length and the number of mid-winter break-ups. The study covers two key sites: Lake St. Lawrence downstream of Kingston, in the international (Canada/USA) section of the river, and Canal Beauharnois, near Montreal. An ice presence prediction method was developed based on air temperature thresholds and validated against historical ISI records. This prediction method was applied, using input data from future climate scenarios, to provide information on the effects of projected climate change on the ice cover at the key sites. The modelling of future ice presence was performed using two sets of air temperature input data (climate scenarios RCP 4.5 and 8.5) over two periods (2040-2060 and 2080-2100). The predictions indicate an ongoing reduction in the ice season length due to progressively later season starts and earlier season ends. A modest increase in the number of freeze-up/break-up cycles is possible for the 2040-2060 period
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".