Long-term drought sensitivity of trees in second-growth forests in a humid region
Bibliographic record
Abstract
Classical field methods of reconstructing drought using tree rings in humid, temperate regions typically target old trees from drought-prone sites. This approach limits investigators to a handful of species and excludes large amounts of data that might be useful, especially for coverage gaps in large-scale networks. By sampling in more “typical” forests, network density and species diversity would increase in ways that could potentially improve reconstructions. Ten nonclassical tree-ring chronologies derived from randomly selected trees, trees from logged forests, or both were compared to more classical chronologies and an independent regional drought reconstruction to determine their usefulness for dendrohydroclimatic research. We find that nonclassical chronologies are significantly correlated to classical chronologies and reconstructed drought over the last 2–3 centuries. While nonclassical chronologies have spectral properties similar to those from classical dendroclimatic collections, they do lack spectral power at lower frequencies that are present in the drought reconstruction. Importantly, our results show that tree growth is strongly dependent on moisture availability, even for small, randomly selected trees in cut forests. These results indicate that there could be more data available in areas with few current tree-ring collections for studying climate history and that drought plays an important role in humid forests.
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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 | 0.001 |
| 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) | 0.000 | 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 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".