Bibliographic record
Abstract
In coastal zones, a major objective of groundwater management is often to determine sustainable pumping rates avoiding well salinization.Understanding how model and climate uncertainties affect optimal management solutions is essential to provide groundwater managers information about salinization risk, and is facilitated by the use of optimization under uncertainty (OUU) methods.However, guidelines are missing for the widespread implementation of OUU in real-world coastal aquifers, and for the incorporation of climate uncertainty into OUU approaches.An ensemble-based OUU approach was developed, considering 15 parameter, observation and climate uncertainty, and was implemented in a real-world island aquifer in the Magdalen Islands (Quebec, Canada).A sharp-interface seawater intrusion model was developed using MODFLOW-SWI2 and a prior parameter ensemble was generated, containing multiple equally plausible realizations.Ensemble-based history matching was conducted using an iterative ensemble smoother and yielded a posterior parameter ensemble conveying both parameter and observation uncertainty.2050 sea level and recharge ensembles were generated and incorporated to generate a predictive parameter ensemble conveying 20 parameter, observation and climate uncertainty.Multi-objective OUU was then conducted, aiming to both maximize pumping rates and minimize probability of well salinization.As a result, the optimal tradeoff between pumping and probability of salinization was quantified, considering parameter, historical observation and future climate uncertainty simultaneously.The multi-objective, ensemble-based OUU led to optimal pumping rates that were very different from a previous deterministic OUU, and close to the current and projected water demand for risk-averse stances.Incorporating climate uncertainty in the OUU was also critical since 25 it reduced the maximum allowable pumping rates for users with a risk-averse stance.The workflow used tools adapted to very high-dimensional, nonlinear models and optimization problems, to facilitate its implementation in a wide range of real-world settings.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.032 | 0.027 |
| Insufficient payload (model declined to judge) | 0.119 | 0.092 |
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".