Strontium isotopic characterization of the Palmottu hydrosystem (Finland): water–rock interaction and geochemistry of groundwaters
Bibliographic record
Abstract
Abstract The Palmottu hydrosystem is located in a granitic host rock in southern Finland. Along well‐defined pathways in the fractured crystalline rock, strontium isotopes are used to trace the degree of water–rock interaction (WRI) and mixing processes in groundwaters. The87Sr/86Sr ratios range between 0.716910 and 0.735606 in the surface waters and between 0.719991 and 0.750787 in the groundwaters, but are between 0.720 and 0.735 in most of the samples. Moreover, the results show a lack of correlation between the water chemistries determining the classification into different water‐types (Na–Cl, Na–SO4, etc.) and the results of the strontium (Sr) contents and Sr isotopic ratios. From a WRI standpoint, this implies that the Sr behaviour is independent of the water chemistry; the occurrence of large87Sr/86Sr variations is site specific and mainly dependent on the lithology. A model to determine the87Sr/86Sr ratio of water after interaction with granite was developed. This model is based on the assumption that Sr was derived from three minerals: plagioclase, K‐feldspar and biotite. The results of the calculation indicate that around half of the water analysed within the Palmottu hydrosystem can be explained by the weathering of the granites. However, clearly lower87Sr/86Sr are observed in waters when compared to the calculated87Sr/86Sr and other sources of Sr, with low87Sr/86Sr, rather than the calculated granite–water interaction, which may be suspected. When comparing the87Sr/86Sr and ion ratios (Ca/Na, Mg/Na, Sr/Na, Cl/Na), the scattering of the data can be explained by the presence of four end‐members: a brine component (low87Sr/86Sr and Ca/Na ratios…), a deep granitic component (high87Sr/86Sr ratios and low Ca/Na ratios…), a subsurface component (intermediate87Sr/86Sr ratios associated with high Ca/Na ratios…) and a surface end‐member:snow and river drainage (low87Sr/86Sr and low Ca/Na ratios…). These extreme end‐members define a series of WRI‐mixing line within a rather complex hydrosystem.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".