Water yield variability and response to climate change across Canada
Bibliographic record
Abstract
This study generated a water yield dataset for Canada for 1979–2016 by subtracting the land surface evapotranspiration (ET) and water surface evaporation (E0) from precipitation (P). The dataset was validated in Budyko space and compared with streamflow (Q) before the spatial variability and trends were analysed. Results indicate (1) uncertainties of the dataset are generally small; (2) despite the asynchronous inter-annual change, annual water yield varies in a similar temporal pattern to Q; (3) annual water yield varies dramatically across Canada, ranging from about zero on the Canadian Prairies to over 2500 mm on the West Coast; and (4) annual water yield shows no significant changes over the study period in the vast majority (82.4%) of Canada’s landmass. The most significant increasing trend appears in South Central Canada, attributed to increasing P. The most significant decreasing trend appears in Northeast Canada and the Southern Montane Cordillera, attributed to decreasing P and increasing ET.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".