Precipitation data quality and long‐term water balances within the Moose River Basin, east‐central Canada
Bibliographic record
Abstract
Annual water balance time series (precipitation, runoff, evaporation) for sub‐basins in the southern portion of the Moose River Basin (MRB) of northeastern Ontario and western Quebec previously suggested that both annual evaporation and precipitation increased by ∼2 mm y−1 during the 1918 ‐1994 period. However, summer air temperature data were not consistent with evaporation increases of that magnitude. Suspected inhomogeneities (i.e., non‐climatic steps or trends associated with changes in measurement procedure or site exposure) in precipitation records for the MRB region indicated significant underestimation of annual precipitation prior to 1950. Temporal trends in rehabilitated precipitation datasets obtained from the Meteorological Service of Canada revealed that annual precipitation was essentially constant during the 1918 ‐ 1994 period in the MRB, after several inhomogeneities were accounted for. This contradicts previously reported increases in precipitation for the region, and results indicate that stream/low time series from large river basins can assist assessment of the validity of apparent precipitation trends. Although there were no trends in long‐term annual precipitation and runoff records, preliminary analyses revealed trends at the seasonal scale. In particular, decreased annual snowfall was offset by increased annual rainfall, which is broadly consistent with predicted impacts of climate warming. Annual evaporation appears to have been relatively constant in the southern MRB during the 1918 – 1994 period, despite changes in the partitioning of precipitation between rain and snow and the seasonal timing of precipitation, and slight increases in summer daytime air temperatures.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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".