Intra-annual ring width and climate response of red pine in Itasca State Park in north-central Minnesota
Bibliographic record
Abstract
Red pine (Pinus resinosa Ait.) of northern Minnesota are part of a growing network of tree-ring chronologies aimed at understanding climate dynamics in the Upper Great Lakes Region. Red pine has been widely used in tree-ring studies of fire and climate variability across its range. Earlier studies have relied primarily on total annual ring width. Here we develop annual and subannual (i.e., earlywood, latewood, and adjusted latewood) chronologies from Itasca State Park to refine our understanding of red pine climate response. Our chronologies extend to the early 18th century and display common growth and cross-dating characteristics indicative of a significant common controlling mechanism. We found that total ring width contains dampened attributes reflective of both the temperature-limited earlywood and moisture-dependent latewood chronologies. The strongest relationship between climate and radial growth is between the adjusted latewood chronology and 3-month summer precipitation, suggesting that overall summer wetness rather than any single summer month primarily limits growth. The ability to disaggregate and improve upon the mixed climate signal of red pine highlights the potential of using intra-annual chronologies to strengthen future climate reconstructions. We hope the methodologies demonstrated here serve as a potential guide for future red pine chronology development in the region.
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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 | 0.000 |
| 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) | 0.001 | 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".