Temporal and spatial variability of tidal‐fluvial dynamics in the St. Lawrence fluvial estuary: An application of nonstationary tidal harmonic analysis
Bibliographic record
Abstract
Abstract Predicting tides in upstream reaches of rivers is a challenge, because tides are highly nonlinear and nonstationary, and accurate short‐time predictions of river flow are hard to obtain. In the St. Lawrence fluvial estuary, tide forecasts are produced using a one‐dimensional model (ONE‐D), forced downstream with harmonic constituents, and upstream with daily discharges using 30 day flow forecasts from Lake Ontario and the Ottawa River. Although this operational forecast system serves its purpose of predicting water levels, information about nonstationary tidal‐fluvial processes that can be gained from it is limited, particularly the temporal changes in mean water level and tidal properties (i.e., constituent amplitudes and phases), which are function of river flow and ocean tidal range. In this paper, a harmonic model adapted to nonstationary tides, NS_TIDE, was applied to the St. Lawrence fluvial estuary, where the time‐varying external forcing is directly built into the tidal basis functions. Model coefficients from 13 analysis stations were spatially interpolated to allow tide predictions at arbitrary locations as well as to provide insights into the spatiotemporal evolution of tides. Model hindcasts showed substantial improvements compared to classical harmonic analyses at upstream stations. The model was further validated by comparison with ONE‐D predictions at a total of 32 stations. The slightly lower accuracy obtained with NS_TIDE is compensated by model simplicity, efficiency, and capacity to represent stage and tidal variations in a very compact way and thus represents a new means for understanding tidal rivers.
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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 | 0.000 | 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 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".