Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
植被物候对当前和未来全球气候变暖的响应, 直接影响森林生态系统初级生产力的变化. 然而, 由于树木木质部物候的监测数据十分稀少, 学界较少关注树木木质部物候的变化, 这阻碍了对森林大空间尺度碳收支的评估和预测. 本研究使用基于生理生态过程的树轮生长模型(Vaganov-Shashkin模型)模拟1962~2016年中国森林站点春季树木木质部物候(木质部生长季开始时间)的时空变化. 使用实测树轮宽度序列(70条)对树轮生长模型模拟结果进行校正, 校正后的模拟结果与已有独立的微树芯监测结果显著相关. 模拟结果发现, 全球变暖背景下春季木质部物候在1962~2016年期间显著提前, 其提前速率在20世纪90年代后显著增快, 平均每年提前0.25天. 生长季前的日均温与大多数站点(71%)的生长季开始时间显著相关, 表明其可能是中国树轮站点春季木质部物候的主要气候驱动因子. 春季增暖的气温允许树木生长的热量需求更早达到, 因此导致春季木质部物候显著提前. 生长季前日均温每升高1℃, 春季物候提前6~7天, 这将有益于偏冷湿环境下树木的径向生长. 模拟的春季树木木质部物候与遥感物候间存在显著正相关, 表明全球增暖背景下树木初生生长和次生生长可能受到气温主控而同步变化. 基于生理过程的树木模拟能够提供长时间和大空间尺度上树木木质部的时空变化, 为中国树木木质部物候对气候变化的响应提供长期视角.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.068 |
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 it