Linking physiography and evaporation using the isotopic composition of river water in 16 Canadian boreal catchments
Bibliographic record
Abstract
Abstract Highly seasonal boreal catchments are hydrologically complex and generally data poor and, hence, are ripe for investigation using tracer‐aided hydrologic models. The influence of physiography on isotopic metrics was assessed to identify the catchment characteristics dominating evaporative enrichment. A multiyear stable isotope of water dataset was collected at the outlets of 16 boreal catchments in central Canada ranging in area from 12 to 15,282 km2. Physiographic characteristics were obtained through raster analysis of freely available land cover images, stream networks, and digital elevation models. Correlation analysis indicated that as the percentage coverage of open water increased, so too did the evaporative effects observed at the catchment outlet. Correlation to wetland metrics indicated that increasing the percentage coverage of wetlands can reduce or increase evaporative effects observed, depending on the isotopic metric used and the corresponding drainage density, catchment slope, and presence of headwater lakes. The slopes of river evaporative‐mixing lines appear to reflect multifaceted relationships, strongest between catchment slope, headwater lakes, and connected wetlands, whereas mean line‐conditioned excess is more directly linked to physiographic variables. Hence, the slopes of river evaporative‐mixing lines and mean line‐conditioned excess are not interchangeable metrics of evaporative enrichment in a catchment. Relationships identified appear to be independent of catchment scale. These results suggest that adequate inclusion of the distribution of open water throughout a catchment, adequate representation of wetland processes, catchment slope, and drainage density are critical characteristics to include in tracer‐aided hydrologic models in boreal environments in order to minimize structural uncertainty.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".