18O and 2H in streamflow across Canada
Bibliographic record
Abstract
Water samples for isotopic analysis were collected during 2013–2019 at 331 gauging stations across Canada in representative watersheds ranging from the Atlantic to the Pacific to the Arctic Oceans. Drainage area coverage of the network included 56 % of Canada’s landmass (9,984,670 km2) and was representative of 91 % of Canada’s annual water yield. Baseline data, including 4603 18O and 2H analyses, are described to assess potential for process studies and predictive model calibration. While similar patterns are noted between isotopes in streamflow and precipitation across Canada, systematic evaporative enrichment in streamflow occurs in lake- and wetland-rich areas, and systematic depletion occurs in some mountainous and/or cold-regions watersheds. The latter are attributed to uncertainty in precipitation isotope records, glacial melt and/or permafrost thaw. In δ18O-δ2H space, streamflow characteristically plotted on or below the Canadian Meteoric Water Line (CMWL) (δ2H = 8∙δ18O+8.5) along imbricated Regional River Lines (RRL) displaying a range of regression slopes (4.34–9.31) and intercepts (-54 to +24), reflecting regional variations in isotopic composition of input sources, evaporative enrichment, and tributary mixing. We define the Canadian Rivers Line (CRL) based on the linear regression of flow-weighted mean values of station data (δ2H = 7.89∙δ18O+0.45, r2 = 0.962; n = 161).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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".