Bibliographic record
Abstract
<strong class="journal-contentHeaderColor">Abstract.</strong> Effectively remediating groundwater contamination relies on the precise determination of its sources. In recent years, a growing research focus has been placed on concurrently estimating hydrogeological characteristics and locating pollutant origins. However, the identification of precise synergistic identification of point and areal contamination sources of groundwater and combined hydrogeological parameters has not been effectively solved. This study developed an inversion framework that integrates machine learning surrogates with the artificial hummingbird algorithm (AHA). The surrogate models approximating the simulation system were constructed using both backpropagation neural networks (BPNN) and Kriging techniques. The AHA was then employed to solve the optimized model, and its performance was benchmarked against particle swarm optimization (PSO) and the sparrow search algorithm (SSA). The applicability of this inversion framework was assessed by application to point sources of contamination (PSC) and areal source contamination (ASC). The robustness of the framework was verified through application to scenarios with different noise levels. The results showed that surrogate model constructed by the BPNN method provided estimates that were closer to those of the simulation model in comparison to the kriging method, coefficient of determination (R<sup>2</sup>) is 0.9994 and mean relative error (MRE) is 3.70 % in PSC, and R<sup>2</sup> is 0.9989 and MRE is 4.48 % in ASC. The performance of the AHA exceeded those of the PSO and the SSA. In PSC, MRE of the identification result is 1.58 %; In ASC, MRE of the identification result is 2.03 %, with the AHA able to rapidly and accurately identify the global optimum and improve the inversion efficiency. The proposed inversion framework was demonstrated to apply to both groundwater PSC and ASC problems with strong robustness, providing a reliable basis for groundwater pollution remediation and management.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".