Resiliency of Silica Export Signatures When Low Order Streams Are Subject to Storm Events
Bibliographic record
Abstract
Abstract Silicon stable isotope ratios ( 30Si) of over 150 stream water samples were measured during seven storm events in six small critical zone observatory (CZO) catchments spanning a wide range in climate (sub‐humid to wet, tropical) and lithology (granite, volcanic, and mixed sedimentary). Here we report a cross‐site analysis of this dataset to gain insight into stream 30Si variability across low‐order catchments and to identify potential climate (i.e., runoff), hydrologic, lithologic, and biogeochemical controls on observed stream Si chemical and isotopic signatures. Event‐based 30Si exhibit variability both within and across sites (−0.22‰ to +2.27‰) on the scale of what is observed globally in both small catchments and large rivers. Notably, each site shows distinct 30Si signatures that are preserved even after normalization for bedrock composition. Successful characterization of observed cross‐site behavior requires the merging of two distinct frameworks in a novel combined model describing both non‐uniform fluid transit time distributions and multiple fractionating pathways in application to low‐order catchments. The combined model reveals that site‐specific architecture (i.e., biogeochemical reaction pathways and hydrologic routing) regulates stream silicon export signatures even when subject to extreme precipitation events.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".