Bibliographic record
Abstract
20 Model calibration 21 Spectral analysis 22 Flow reversal 23 Intake protection zonesQ4 24Eastern Lake Ontario and the upper St. Lawrence River provide drinking water for approximately 175,000 25people. To understand the flow dynamics surrounding the eight drinking water intakes in this region, the 26hydrodynamics were simulated using the Estuary and Lake Computer Model (ELCOM) for the period April– 27October 2006. Model simulated water levels, temperatures, and current velocities were compared with 28observations. Root-mean-square errors in temperature and current simulation were ~2 °C and 29~5–8 cm s−1, respectively. Normalized Fourier norms ranged from 0.8 to 1.2. These errors are consistent 30with other applications of Reynolds-averaged models to the Great Lakes. ELCOM thus reasonably captures 31the dynamics of the flow regimes in the nearshore region. The flow was found to be predominantly wind 32induced in the southwestern lacustrine portion of the domain, with observed but not modeled weak near-33inertial oscillations, and hydraulically driven in the northeastern riverine portion. Diurnal and semi-diurnal 34forcing influenced the flow throughout the domain. Flow reversal of the St. Lawrence River near Kingston 35occurred during strong easterly storm events. The model results were applied to delineate Intake Protections 36Zones surrounding the municipal drinking water intakes.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.018 |
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".