Climate-sensitive self-thinning trajectories of Chinese fir plantations in south China
Bibliographic record
Abstract
The self-thinning rule is fundamental in regulating maximum stocking and constructing stand density management diagrams. Chinese fir (Cunninghamia lanceolata (Lamb.) Hook.) is the most important tree species and widely distributed across subtropical China. Yet, our understanding of how the self-thinning line of Chinese fir relates to climate is limited. Longitudinal data from 48 plots distributed in Fujian, Jiangxi, Guangxi, and Sichuan provinces were used to describe self-thinning for Chinese fir in relation to climate through first-order autoregressive (AR(1)) and nonlinear mixed effects (NLME) models. Results showed that self-thinning lines had steeper slopes for Chinese fir growing in areas with larger annual precipitation and summer mean maximum temperature but flatter slopes with higher mean annual temperature, degree-days below 0 °C, and winter mean minimum temperature. Winter mean minimum temperature was the dominant climatic factor in shaping self-thinning lines, which suggests that temperature was the key climate driver that affects self-thinning of Chinese fir. In addition, differences of slopes for any two of the four sites were significant, except between the Guangxi and Sichuan sites. Our results will be useful for both the silvicultural practices and mitigation strategies of Chinese fir under climate change in south China.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".