Analysis of fragmented piezometric levels records: the ARTE (Antecedent Recharge Temporal Effectiveness) approach
Bibliographic record
Abstract
In contrast to climatic data, piezometric records are often fragmented both in time and space continuity, despite their crucial importance in groundwater studies. This work presents a new method for analysis of groundwater level vs. recharge processes relation from fragmented piezometric data, called Antecedent Recharge Temporal Effectiveness (ARTE). The ARTE method was tested on 5 year-long (2016-2020) water table level datasets measured by three automatic piezometers located in the Lucca plain (Tuscany, Italy). For each piezometric level time series, measurements were extracted every 30, 60, and 120 days, and randomly, obtaining fragmented records inlcuding less than 3% of the complete time series. As for recharge processes of the monitored aquifer, rainfall and riverbed infiltration were selected. Hence, daily rainfall and daily mean river stage time series were acquired from different automatic raingauges and hydrometers respectively. The relationship between these recharge processes and the variation of the piezometric level from the artificially fragmented datasets were evaluated with the ARTE method. The ARTE method was potentially able to identify maximum correlation time intervals, for which the recharge processes are most likely to influence the groundwater level. Based on the analysis conducted on the fragmented piezometric datasets, the reconstruction of each piezometric time series was attempted for the study period. The simulated daily groundwater level records have RMSE values between 0.21 m and 0.73 m and NRMSE values between 0.08 and 0.16, which are satisfactory results when compared with other more complex simulation procedures, in which the training datasets are increasingly larger.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".