Determining the Regional Geochemical Background for Dissolved Trace Metals and Metalloids in Stream Waters: Protocol, Results and Limitations—The Upper Loire River Basin (France)
Bibliographic record
Abstract
To avoid the improper disqualification of a watershed for which the water–rock interaction (WRI) may produce trace element concentrations exceeding established guidelines, it is of the utmost importance to properly establish natural geochemical backgrounds. Using the example of the crystalline Upper Loire River Basin, we are proposing a methodology based on the selection and chemical characterization of water and sediment samples from 10 monolithologic watersheds supposedly lowly impacted by anthropogenic inputs. We collected water samples from each watershed’s spring down to its outlet and measured dissolved major, minor and selected trace elements (Al, As, Ba, Cd, Co, Cr, Cs, Cu, La, Ni, Pb, U, V and Zn) at low- and high-water stages. Results show that the chemical signature of the stream waters is controlled by mineral weatherability rather than by the available rock stock. As a result, the variability in dissolved metal concentrations between the principal lithologies is similar to that observed within each of them. While some elements mostly result from WRI, others clearly identify high inputs from topsoil leaching. Comparison with published data evidences the need to subdivide studied watersheds into distinct sectors, according to the distance from the spring, in order to define reliable natural backgrounds.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".