Bibliographic record
Abstract
This study develops a water-level management model for the Great Lakes using a predictive control framework. Requirement 1: Historical data (pre-2019) revealed consistent monthly water-level patterns. A simulated annealing algorithm optimized flow control via the Moses-Saunders Dam and Compensating Works to align levels with multi-year benchmarks. Requirement 2: A Water Level Predictive Control Model (WLPCM) integrated delayed differential equations (DDEs) and model predictive control (MPC) to account for inflow/outflow dynamics and upstream time lags. Natural variables (e.g., precipitation) were modeled via linear regression, while dam flow rates were optimized over 6-month horizons with feedback adjustments for robustness. Requirement 3: Testing WLPCM on 2017 data successfully mitigated Ottawa River flooding, outperforming historical records. Sensitivity analysis via the Sobol method confirmed model resilience to parameter variations. Requirement 4: Ice-clogging was identified as the most impactful natural variable (via RMSE-based sensitivity tests), followed by snowpack and precipitation. Requirement 5: Stakeholder demands (e.g., flood prevention, ecological balance) were incorporated into a fitness function. Compared to Plan 2014, WLPCM reduced catastrophic high levels in Lake Ontario and excessive St. Lawrence River flows by prioritizing long-term optimization. Key innovations include DDE-based predictive regulation, real-time feedback loops, and adaptive control under extreme conditions. The framework balances hydrological dynamics, stakeholder needs, and uncertainty management, offering a scalable solution for large freshwater systems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".