Tidal dynamics in channels: 2. Complex channel networks
Bibliographic record
Abstract
Intricate networks of tidal channels such as those found in fjordic, barrier island, and branching estuarine systems are often at risk from contaminant inputs and can be important as spawning grounds or migration pathways for marine organisms. These intricate systems are rarely spatially resolved in regional‐scale numerical tidal models, and setting up a specific detailed numerical model for the purpose of rapidly assessing the likely tidal behavior of such geometrically complex systems carries a high overhead. Here we describe a straightforward algorithm (implemented in MATLAB) which permits rapid assessment of the linear tidal dynamics in an arbitrarily complex network of tidal channels. The method needs only a minimum of input data, namely, (1) the forcing tidal elevation amplitude and phase at the entrances of those channels directly exposed to the open sea and (2) the length, width, and depth of each channel. The performance of the method is tested against results from the finite element regional‐scale numerical model of Foreman et al. (1993) in the fjordic region of western Canada.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".