Tackling Voids in Observations: An Approach to Reconstruct Rainfall <sup>35</sup>S Time Series from Proxy Parameters
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Due to its short half-life (87.4 days) and omnipresence, cosmogenic radio-sulfur ( 35 S) is an attractive tracer for investigating subyearly groundwater residence times. 35 S is transported to the lower atmosphere by large-scale air mass circulation and transferred to groundwater by precipitation. For groundwater dating, the variability of 35 S concentration in precipitation requires a 35 S input function. However, the required 35 S data are often not available. To fill this gap, we present an approach to reconstructing 35 S concentrations in precipitation based on proxy parameters of better availability. The tested parameters include natural 7 Be and 3 H, parameters that allow quantifying cosmogenic 35 S production, and parameters that are correlated to the intensity of 35 S washout from the atmosphere. In comparison with an unrivaled 4 year time series of 35 S in precipitation, we discuss the correlations of all parameters with 35 S, assess their individual applicability as 35 S proxies, and evaluate the predictive power of joint data sets in varying combination. As a result, we present a modeling approach that allows reconstruction of a 35 S input function with a monthly temporal resolution based on proxy parameters. This novel modeling approach provides a valuable tool for groundwater dating using 35 S as a tracer in studies that lack directly measured 35 S input 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".