Development of Site Specific Ice Growth Models for Hydrometric Purposes
Bibliographic record
Abstract
The Water Survey of Canada (WSC) has put together a database containing hydrometric measurements from various sources including data collected from routine field operations in both open water and under ice, and FUI (Flow Under Ice), a joint project between WSC and USGS containing winter hydrometric measurements. Field records of water surface to bottom of ice measurements, under ice discharge, meteorological records, and other pertinent information about river ice contained in this database were examined. Analysis of the data revealed information about the effect of climate variables such as snow cover, snow density, cumulative freezing degree-days, and solar radiation on river ice growth. The influence of the weight of a snow cover on top ice growth was also examined. Sites that were investigated include sites in Alberta, Ontario, and the Northwest Territories. A site specific statistical ice growth model was developed for each of 11 hydrometric stations based on water surface to bottom of ice records found in the database, current ice growth theory, and climate records. These ice growth models were evaluated using statistical analyses and they were also validated against measured data collected during the winters of 2003-2004 and 2004-2005. Six of the models developed were found to adequately predict river ice growth. Five of the models did not predict river ice growth adequately. The inadequacy of some of the models is primarily due to the fact that insufficient data were available for these locations, there was uncertainty in the accuracy of the climate data, and there were inconsistent measurement locations. In order to develop more accurate models, it is recommended that measurement locations be recorded, that climate sensors be installed at hydrometric stations located far from climate stations, and that the effect of velocity and other hydraulic parameters on ice thickness be examined.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".