Dendroclimatic signals deduced from riparian versus upland forest interior pines in North Karelia, Finland
Bibliographic record
Abstract
Abstract Radial growth of boreal tree species is only rarely studied in riparian habitats. Here we investigated chronologies of earlywood, latewood, and annual ring widths and blue intensity (BI; a surrogate to latewood density) from riparian lake shore and upland forest interior pines ( Pinus sylvestris L.) growing in boreal forest in eastern Finland. Riparian and upland chronologies were compared to examine differences in the pine growth variability and growth response to climatic variation in the two habitats. It was found that the climatic variables showing statistically significant correlations with the tree‐ring chronologies were related to snow conditions at the start of the growing season. Deeper snowpack led to reduced upland pine growth, possibly due to delayed snowmelt and thus postponed onset of the growing season. Warm late winters were followed by increased riparian pine growth because of earlier start of the snow‐melt season and thus a lower maximum early summer lake level. Moreover, riparian pines reacted negatively to increased rainfall in June, whereas the upland pines showed a positive response. Latewood growth reacted significantly to summer temperatures. The BI chronology showed a strong correlation with warm‐season temperatures, indicating an encouraging possibility of summer temperature reconstruction using middle/south boreal pine tree‐ring archives.
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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) | 0.001 | 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".