Spatial-temporal migration dynamics of submarine dunes in St. Lawrence River
Bibliographic record
Abstract
The seabed of the St. Lawrence River in Quebec City is a high-energy fluvial sedimentary environment with a mean annual discharge of 12,600 m3/s, the second largest in North America [1]. Significant changes can be observed in the seabed over short time scales, i.e, daily to weekly. These bathymetric changes on the bottom of St. Lawrence River and estuary represent a constant risk for the navigation, considering that surveys cannot be undertaken between November and April. Prediction models are necessary to anticipate the risks and the critical changes in the seabed. The development of a prediction model of dunes migration is the focus of this research. To better understand the dunes migration in the seabed, it is necessary to estimate thee quality of the acquired data in the context of the dunes. Many research works developed the error budget to bathymetric data, such as [2], [3] and [4]. The Combined Standard Measurement Uncertainty (CSMU) model presented in this paper considers the uncertainties associated with the observations made by the bathymetric system as well as the geometric uncertainty as developed by [5] for Mobile LiDAR Systems.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".