Bibliographic record
Abstract
Abstract. For enhanced public safety and water resource management, a three-dimensional operational lake hydrodynamic forecasting system, COASTLINES (Canadian cOASTal and Lake forecastINg modEl System), was developed. The modeling system is built upon the three-dimensional Aquatic Ecosystem Model (AEM3D) model, with predictive simulation capabilities developed and tested for a large lake (i.e., Lake Erie). The open-access workflow derives model forcing, code execution, post-processing, and web-based visualization of the model outputs, including water level elevations and temperatures, in near-real time. COASTLINES also generates 240 h predictions using atmospheric forcing from 15 and 25 km horizontal-resolution operational meteorological products from the Environment Canada Global Deterministic Forecast System (GDPS). Simulated water levels were validated against observations from six gauge stations, with model error increasing with forecast horizon. Satellite images and lake buoys were used to validate forecast lake surface temperature and the water column thermal stratification. The forecast lake surface temperature is as accurate as hindcasts, with a root-mean-square deviation <2 ∘C. COASTLINES predicted storm surges and up-/downwelling events that are important for coastal flooding and drinking water/fishery management, respectively. Model forecasts are available in real time at https://coastlines.engineering.queensu.ca/ (last access: January 2022). This study provides an example of the successful development of an operational forecasting workflow, entirely driven by open-access data, that may be easily adapted to simulate aquatic systems or to drive other computational models, as required for management and public safety.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.019 | 0.009 |
| Insufficient payload (model declined to judge) | 0.262 | 0.217 |
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".