Investigating hydrologic controls on 26 Precambrian shield catchments using landscape, isotope tracer and flow metrics
Bibliographic record
Abstract
Abstract Although many studies focus on catchment classification, hydrologists are challenged to find the most important variables for a meaningful catchment classification, especially in the heterogeneous environment of the Precambrian Shield. This study investigates landscape controls on hydrologic response by conducting a catchment classification of 26 catchments located within two Precambrian Shield watersheds (Sturgeon–French–Nipissing [SNF] and Muskoka) in central and Northeastern Ontario, Canada, using combinations of landscape characteristics (e.g. topography, geology, landcover), hydrometric variables, and stable isotopes of δ18O and δ2H in river flow. Weekly to monthly surveys of δ18O and δ2H in river flow were collected between 2013 and 2019. Flow metrics (e.g. Pardé coefficient and coefficient of variation of streamflow) were generated for 14 of the 26 rivers between 2008 and 2018. Principal component analyses (PCA) and Hierarchical Clustering on Principal Components (HCPC) analyses were used to identify variables controlling catchment clustering according to their similarities for four different scenarios. Despite their similar location along the southern edge of the Precambrian Shield, the 26 catchments generally clustered by watershed (SNF and Muskoka) with some exceptions. Differences in wetland and lake area (%), mean slope, and % area covered by glacialacustrine and glaciofluvial outwash deposits were the most influential variables in catchment classification. A positive correlation between % wetland area and streamflow stable isotope damping ratios suggests greater % wetland area (with shallow and potentially seasonally variable surface areas and/or hydrologic connection) observed in the SNF catchments increases variability in the influence of evaporative enrichment in SNF catchments. The catchment classification analyses in combination with stable isotopes of δ18O and δ2H were functional tools to investigate the combined influences of diverse types of catchment characteristics that lead to differences in hydrometric response. These results could support future studies focusing on generating hydrologic models and representing wetlands connectivity in the region.
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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 | 0.002 | 0.002 |
| 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".