Detection of spatial and temporal hydro-meteorological trends in Lake Michigan, Lake Huron and Georgian Bay
Bibliographic record
Abstract
The Laurentian Great Lakes represent the largest freshwater basin on Earth, containing 21% of the world's surface fresh water by volume. Water level fluctuations are an on-going concern and have received considerable attention in the area. We present a trend analysis of meteorological (air temperature, cloud cover, and wind speed) and hydrological (precipitation, runoff, and evaporation) variables for Lake Michigan, Lake Huron, and Georgian Bay. Using the non-parametric Mann-Kendall test, our analysis identified significant upward trends in daily minimum air temperature, whereas daily maximum air temperature demonstrated weakly decreasing trends in space and time. Evaporation was found to be increasing from late spring until early fall and this pattern may be explained by the shortening of the ice/snow cover period, which results in faster warming of lake surface due to the induced variations in albedo feedback. Time-series analysis of the over-lake precipitation revealed mostly non-significant statistical trends. Recent temperature increases may have led to elevated winter runoff in the Great Lakes region, given that precipitation falls mainly as rain instead of snow. We also provide clear evidence of reduced cloud cover and wind speed. Our study offers critical insights into the patterns of within- and among-year variability of hydro-meteorological variables useful in elucidating the mechanisms that modulate water levels in the Great Lakes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".