Hydrodynamic model of St. Clair River with Telemac-2D: phase 2 report
Bibliographic record
Abstract
As part of the International Upper Great Lakes Study, a two dimensional numerical model of St. Clair River was developed using Telemac software (ref 1). This model was well calibrated with the multi-beam 2002 bathymetry survey in the upper portion of the River, the 2000 single-beam survey for its lower portion, and the existing stage-discharge relationships. It has a very fine grid mesh in the upper portion of the river (15 m) which allows the proper description of small bottom irregularities. It was used extensively to simulate changes, such as bathymetry, morphology, and bed material content or its bottom friction, which may have occurred in the last 35 years, and to assess the impacts of these changes on the river hydrodynamics. In order to improve the model range of applicability, the following modifications were performed: o Check the model transect velocity profiles, with ADCP velocity measurements in cross-sections downstream from Blue Water Bridge, in order to verify the size and strength of current recirculation. o Recalibrate using the 2007 multi-beam bathymetric survey The model was then used to o Verify model range of applicability using monthly average data, in a wider range of flows and levels. o Re-calibrate the model with measurement data available from the 1971 era, (flow and levels). o Compare 1971/2007 river hydrodynamics using the two 1971 and 2007 models. o Assess sensitivity of the quality of the input data on the results
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".