Modeling frazil ice growth in the St. Lawrence River
Bibliographic record
Abstract
The paper presents a complete energy balance model of suspended frazil ice formation in the tidal water column of the St. Lawrence River. The model estimates of suspended frazil ice concentration are compared to in situ observations of an analogue of suspended frazil ice crystals, with good results. A time series of observed acoustic backscattering from suspended frazil serves as the analogue of the suspended frazil concentration. The model of frazil ice growth is used to estimate the rate of increase in mass of the suspended frazil ice by balancing the net rate of energy exchange with the atmosphere with the observed changes in water temperature and in anchor ice thickness. The positive results are achieved despite a number of difficulties with analysis. These difficulties include the bias resulting from the inability of the sonar to detect across the complete size range of suspended ice, the unknown impacts of advection, the unknown impact of anchor ice build-up on temperature and sonar readings, and the lack of knowledge regarding the accuracy of sonar estimation of anchor ice thickness. These results highlight the promise of models, suitably applied, and sonar to quantitatively estimate suspended frazil ice concentration.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".