Numeraical twins and deep neural network to predict groundwater flow 
Bibliographic record
Abstract
Groundwater is and will increasingly be under threat due to many anthropic stresses like climate changes, population growth in coastal cities, pollution,... It is known that realistic 3D numerical twins of aquifers allows forecasting their groundwater flow and permits to forecast their behavior in regards to different hydrogeological changes. In this project, we built an ensemble of numerical twins of an aquifer located south-east to Montreal, Qc, Canada, using a nested geostatistical workflow in order to optimize a pump and treat plant constrain by multiple environmental indicators. The ensemble permits to obtain a quantitative measure of the uncertainty for each indicator base on the optimization of the ensemble. While these models have proved to be useful operationally speaking, any changes or scenarios that must be tested requires the managers of the resources to hire qualified companies. This prevents the long term use of the numerical twins and reduce their democratisation to the resource management. This motivates the training of a deep neural graph network on the numerical twins. The trained network is able to forecast short term changes of the groundwater flow due to new pumping rates or new pumping wells in less than a minute.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".