Cross-CZO storm-event stream water silicon stable isotope - discharge datasets from manuscript (USA, France, Canada)
Bibliographic record
Abstract
*** This is the addendum version with updated fSi_diss *** This resource contains accompanying stream water chemistry data (major cations, anions, DOC, Silicon, Germanium, and Silicon stable isotopes) presented in "Resiliency of silica export signatures when low order streams are subject to storm events" by Fernandez et al. The datasets represent seven storm events from six different catchments across the US, Canada, and France as part of an international cross-CZO collaboration supported through SAVI (Science Across Virtual Institutes). Files are provided in ".csv" format and are organized both with respect to individual sites (listed below) and as bulk metadata (designated 'AllSites' ). CZO sites and storm events investigated for this study (site specific csv file name identifier is provided inside the brackets [ ] ): [LaJara] - La Jara Creek, Jemez CZO (New Mexico, USA) - March to Late May 2017 snowmelt event [ProvidenceCreekP303] - Providence Creek Subcatchment P303, Southern Sierra CZO (California, USA) - January 2018 rain event [ElderCreek] - Elder Creek, Eel River CZO (California, USA) - January 2017 rain event [Sapine ] - Sapine Creek, OZCAR (Mont Lozère, FRA) - October 2016 rain event and long term monthly sampling from 2013-2015 [QuiockCreek] - Quiock Creek, OZCAR (Basse-Terre, Guadeloupe, FRA) - October 2015 rain event [KwakshuaWatershed708] - Kwakshua Watershed 708, Hakai Institute (Calvert Island, British Columbia, CAN) - two rain events, September and October 2017 Site-specific lithology and associated chemical compositions used in calculations for fSi (proxy for fraction of Si remaining in solution) are provided in 'bedrock_compositions.xlsx'. Refer to the manuscript for further details.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.038 |
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".