Sizeable net export of base cations from a Carpathian flysch catchment indicates their geogenic origin while the 26Mg/24Mg, 44Ca/40Ca and 87Sr/86Sr isotope ratios in runoff are indistinguishable from atmospheric input
Bibliographic record
Abstract
Abstract Nutrient imbalances may negatively affect the health status of forests exposed to multiple stress factors, including drought and bark beetle calamities. We studied the origin of base cations in runoff from a small Carpathian catchment underlain by base-poor flysch turbidites using magnesium (Mg), calcium (Ca) and strontium (Sr) isotope composition of 10 ecosystem compartments. Our objective was to constrain conclusions drawn from long-term hydrochemical monitoring of inputs and outputs. Annual export of Mg, Ca and Sr exceeds 5-to-15 times their atmospheric input. Mass budgets per se thus indicate sizeable net leaching of Mg, Ca and Sr from bedrock sandstones and claystones. Surprisingly, δ26Mg, δ44Ca and 87Sr/86Sr isotope ratios of runoff were practically identical to those of atmospheric deposition and soil water but significantly different from bedrock isotope ratios. We did not find any carbonates in the studied area as a hypothetical, easily dissolvable source of base cations whose isotope composition might corroborate the predominance of geogenic base cations in the runoff. Marine carbonates typically have lower δ26 Mg and 87Sr/86Sr ratios, and silicate sediments often have higher δ26Mg and 87Sr/86Sr ratios than runoff at the study site. Mixing of these two sources, if confirmed, could reconcile the flux and isotope data.
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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 | 0.000 | 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) | 0.002 | 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".