Multi-isotopic regional-scale screening on drinking groundwater in Lombardy Region (Italy)
Bibliographic record
Abstract
Groundwater is the main and safest source of water used for drinking purposes in many urban and rural communities worldwide. A deep knowledge of aquifer systems in terms of quality, vulnerability and renewability is fundamental to preserving groundwater resources. Thanks to contributions by different members of Water Alliance in synergy with Sapienza University, during November 2019 a multiisotopic regional scale analysis was carried out on groundwater tapped for drinking purposes in a wide area of the Lombardy Region. The study aimed to improve knowledge of recharge mechanisms, the groundwater’s relative age, and the impact of human activities on groundwater quality. Each Water Alliance supplier selected some wells and springs drawing water from different aquifer groups and distributed from north to south, for a total of 121 samples. Groundwater stable isotope analyses were performed on all the monitoring points, while tritium, nitrogen isotopes (15N and 18O in nitrates), sulphate isotopes (34S and 18O) and 13C isotope in Dissolved Inorganic Carbon (DIC) were analysed in selected monitoring wells based on previous data and major ion concentration results. The results confirm the key role of a multi-isotopic approach in defining aquifer recharge processes, relative groundwater age and the origin of pollutants, offering a useful tool to highlight local issues which could be investigated in depth by each water supplier.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 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".