Characteristics of ice in the St. Lawrence River
Bibliographic record
Abstract
The main objective of this paper is to describe the characteristics of brash ice in the St. Lawrence River downstream of Montréal over a period of three winters. We used two instruments deployed in the St. Lawrence River navigation channel through Lake St. Pierre to measure ice parameters: an acoustic Doppler current profiler (ADCP) and an ice-profiling sonar (IPS). This paper discusses the capacities of these instruments to quantify ice characteristics and to predict the risk of ice congestion. It was found that wind velocity and air temperature play major roles in the variation in ice parameters and, consequently, in the occurrence of ice congestion in the navigation channel through Lake St. Pierre. Comparison of the IPS and ADCP data showed good agreement and demonstrated that these two instruments can be very effective for certain ice applications.Key words: ice characteristics, ice congestion, ADCP, IPS, fuzzy logic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".