Long-Term Climate Change at Four Rural Stations in Minnesota, 1920-2010
Bibliographic record
Abstract
Temperature data from 1920-2010 from four rural Minnesota stations were classified into three 30-year timeframes and examined for differences and trends in temperature (Tmax and Tmin), precipitation, and growing season variables: start of season (SOS), end of season (EOS) and length of season (LOS). These variables were subjected to an ANOVA, and Tukey test to ascertain statistical differences between the 30-year datasets. Subsequently, a Change Point analysis was applied pursuant to temperature and precipitation trends to determine the start and termination of temporal trends occurring within the dataset without the imposition of 30-year timeframes. The main findings were: (1) precipitation has increased significantly in May and June since 1985 and that the last 30-year timeframe possessed more precipitation (69-206 mm/2.7-8.1
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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) | 0.000 | 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 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".