Efficient construction of short-term transfer functions for closed-loop boreholes in stratified aquifers under groundwater flow using neural networks and wavelet decomposition
Bibliographic record
Abstract
Transfer functions often overlook the stratigraphic heterogeneity and groundwater flow commonly found in natural geological settings, as well as the short-term effects from borehole thermal capacities. This study addresses this situation by presenting a combination of three artificial neural networks for the approximation of short-term transfer functions defined at the borehole outlet for a closed-loop borehole embedded in a multilayered geological environment and influenced by groundwater flow. This novel combined model employs a wavelet decomposition scheme as a pre-processing step to enhance the accuracy of the target function, while combining sub-networks to streamline implementation and reduce computation time. The results demonstrate high accuracy and efficiency, with the combined model agreeing well with transfer functions simulated using a 3D finite element model over a range of geological settings, borehole configurations, and operating conditions. The combined model exhibits an average relative root mean square error of 8.81×10−4 on 4371 independent simulations, with prediction times as low as 0.05 ms.
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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) | 0.000 | 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 | 0.000 |
| 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".