Frequency of cool summers in interior North America over the past three centuries
Bibliographic record
Abstract
An innovative technique utilizes a tree‐ring marker to investigate long‐term changes in the frequency of cool summers in Interior North America (INA), a region that currently suffers important gaps in knowledge concerning annual to secular temperature changes. Using multivariate adaptive regression splines (MARS), we established a threshold for the formation of climatically‐induced light rings recorded in Pinus banksiana trees from INA. Then, we used the MARS model to reconstruct negative departures in summer maximum temperatures (April–September) from 1717 to 2007. The estimates explain 45% of the variance in instrumental temperature data. The reconstruction indicates the presence of significant multidecadal changes in the frequency of cool summers, with maximums in 1780, 1900 and 1960 and minimums in 1740, 1860, 1920 and 2000. No evidence of secular changes was found.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".