Effect of intraspecific competition on biomass partitioning of <i>Larix principis-rupprechtii</i>
Bibliographic record
Abstract
It is acknowledged that trees biomass allocation in response to environmental conditions. However, it remains poorly understood what strategies of plant biomass allocation with inter- and intraspecific interactions of tree species in forest stands. Such information is important for revealing strategies of plant biomass allocation with plant competition. To address this problem, a study was conducted in Larix principis-rupprechtii plantations to evaluate the impact of plant competition on plant biomass allocation in Shanxi Province, China. We measured a competition index (CI), stem, branch, foliage, and root biomass as well as element content (Carbon (C), Nitrogen (N), Phosphorus (P), Potassium (K)). Stem-foliage ratio (S/F), aboveground–belowground biomass ratio (T/R), average annual increment of biomass (AAB), height (AAH), and DBH (AAD) were calculated. The study found that the competition intensity of neighboring trees was closely related to the partitioning of biomass. Our results demonstrated that competition pressure of neighboring trees was a crucial factor to drive and regulate the distribution of biomass. Predicting biomass allocation–competition relationships could represent a supportive method for improving management of Larix principis-rupprechtii plantations in Mountain Taiyue areas.
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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 | 0.000 | 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".