Regional coherence of climatic and lake thermal variables of four lake districts in the Upper Great Lakes Region of North America
Bibliographic record
Abstract
1. Within a region with common climatic conditions, lake thermal variables should exhibit coherent variability patterns to the extent to which they are not influenced by lake specific features such as morphometry and water clarity. We tested the degree of temporal coherence in interannual variability for climatic variables (air temperature and solar radiation) among four lake districts in the Upper Great Lakes Region. We also tested the degree of coherence of lake thermal variables (near‐surface temperature, eplimnetic temperature, hypolimnetic temperature and thermocline depth) for lakes within these districts. 2. Our four lake districts included the Experimental Lakes Area in north‐western Ontario, the Dorset Research Centre area north of Toronto, Ontario, the Northern Highland Lake District in northern Wisconsin, and the Yahara Lakes near Madison in southern Wisconsin. Seventeen lakes were analyzed for lake thermal variables dependent on stratification. Another five lakes were added for the analysis of near‐surface temperature. 3. The analysis tested whether for monthly and summer means, the climate (air temperature and solar radiation) across the four lake districts was coherent interannually and whether variables which measure the thermal structure of the lakes were coherent interannually among lakes within each lake district and across the four lake districts. 4. Temporal coherence was estimated by the correlation between lake districts for meteorological variables and between lake pairs for lake thermal variables. Mean coherence and the percentage of correlations exceeding the 5% significance level were derived both within and between lake districts for lake thermal variables. 5. Across the four lake districts, summer mean air temperature was highly coherent while summer solar radiation was less coherent. Approximately 60–80% of the interannual variation in mean summer air temperature at a site occurred across the entire region. Less than 45% of the variation in solar radiation occurred across sites. 6. Epilimnetic temperature and the near‐surface temperature were highly coherent both within and between lake districts. The coherence of thermocline depth within and between lake districts was weaker. Hypolimnetic temperature was not coherent between lake districts for most lake pairs. It was coherent among lakes within some lake districts. 7. The influences of local weather and differences among lakes in water clarity are discussed in the context of differences in levels of coherence among lake thermal variables and among lake pairs for a given variable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 teacher head, 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".