High risk of growth cessation of planted larch under extreme drought
Bibliographic record
Abstract
Abstract Larch trees are widely used in afforestation and timber plantations. Yet, little is known on how planted larch trees cope with increasing drought. We used a tree-ring network of 818 trees from 31 plantations spanning most of the distribution of Larix principis-rupprechtii to investigate how extreme drought influences larch radial growth in northern China. We found that summer drought, rather than temperature or precipitation, had the strongest relationship with radial growth throughout the region. Drought increased in frequency in recent decades, leaving a strong imprint on the radial growth of larch, particularly in dry sites. Across its distribution, radial growth in larch trees that experienced extreme droughts more frequently displayed lower resistance to drought, but higher recovery after it, suggesting these populations were better adapted to extreme droughts. Radial growth decreased with increasing drought, with particularly severe declines below a threshold Palmer Drought Severity Index (PDSI) value of −3 to −3.5. Extreme droughts (PDSI < −4.5) caused a reduction of 62% of radial growth and chronic drought events caused around 20% reduction in total radial growth compared with mean growth on the driest sites. Given that current climate projections for northern China indicate a strong increase in the frequency and severity of extreme drought, trees in large portions of the largest afforestation project in the world, particularly those in the drier edge, are likely to experience severe growth reductions in the future.
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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 | 0.000 | 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) | 0.001 | 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".