The Past Thirty Years of Accomplishment and Future Direction of Canadian River-Ice Science and Engineering
Bibliographic record
Abstract
During the past 30 years, major advancements have been made in understanding the physical processes of river-ice formation, growth and breakup, in developing numerical modelling tools, and in attaining knowledge of winter environments. River-ice science now has a well-established knowledge base. The papers presented at the river-ice workshops of the CGU Committee on River Ice Processes and the Environment (CRIPE) and the ice symposia of the International Association for Hydro-Environment Engineering and Research (IAHR) attest to the advancement in river-ice science and engineering knowledge that has occurred. Despite the great strides made in recent decades, opportunities remain for scientists to do fieldwork and laboratory studies that focus on areas of limited river-ice knowledge. Research needs include river-ice processes in estuaries and tidal rivers, the effects of ice on channel morphology, the hydrology of ice-covered rivers under a changing climate, and the interrelationship of ecological variables during the winter season. The practice of engineering has benefited from the improved understanding of river-ice processes and resulting improvements in numerical modelling. Greater opportunity now exists compared to 30 years ago for the application of river-ice science by civil engineers during the planning, design and operation of hydropower facilities, major water intakes, bridges and other infrastructure along ice-covered rivers. The paper reviews the considerable progress in river-ice science and engineering in Canada during the past 30 years and the implications to civil engineering, aw well as, commentaries on the anticipated direction of river-ice science and engineering during the next decade and research needs.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".