Twig size-number trade-off among woody plants in Tiantong region,Zhejiang Province of China
Bibliographic record
Abstract
枝条大小和数量关系反映了植物适应环境胁迫的构型和生物量分配策略。该研究以浙江天童木本植物为对象，通过对小枝大小（横截面积）与数量（稠密度）关系的研究发现：1）小枝稠密度与枝截面积显著负相关（斜率为-1．32，CI=-1．48--1．17；P〈0．05）；2）在相同曝光度，在Ⅱ级曝光环境（植株40％-80％暴露在直射光中）中常绿植物比落叶植物单位小枝截面积的小枝稠密度高，而在Ⅰ级（植株〈40％暴露在直射光中）和Ⅲ级（〉80％暴露在直射光中）曝光环境中，小枝稠密度在两种生活型间无显著差异；3）在不同曝光度下，常绿植物单位枝条在Ⅰ和Ⅱ级比Ⅲ级曝光水平具有更高的小枝稠密度；但落叶植物单位枝条的小枝稠密度在3个曝光水平相同；4）相同枝条大小下，4m以下灌木比4m以上的亚乔木和乔木具有更高的小枝稠密度。总之，天童地区木本植物的小枝“大小-数量”关系符合Comer法则（描述枝叶“大小-数量”关系的法则），且在不同生活型间存在差异，常绿植物相对于落叶植物，灌木相对于乔木具有较高的小枝稠密度，从而有利于它们适应光资源的限制。
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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 | 0.001 | 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".