Bibliographic record
Abstract
本文通过对云观测卫星-CloudSat的2007～2009年3年的观测资料的分析, 研究了东亚地区的云的垂直结构. 首次计算了在气候模式的云辐射过程中表征云的垂直结构特征的一个重要参数: 抗相关厚度Lcf* 本文结果表明: 6个研究域的抗相关厚度基本处于0～3 km的范围之中, 根据研究子域的云量不同来划分, 抗相关厚度极值出现在云量为0.6～0.8的子域附近, 平均约为2.5 km. 6个研究域的Lcf*纬向差异明显, 处于较高纬度的北方地区和西北地区的Lcf*整体大于较低纬的青藏高原地区和南方地区, 而东部海域和东亚地区介于两者之间. 不同季节之间的差异表明东亚地区研究域和位于东亚地区西部的西北地区, 青藏高原地区和南方地区三个研究域的Lcf*具有夏季最大, 春、秋次之, 冬季最小的特点; 位于东亚地区较东部的东部海域和北方地区研究域的Lcf*则呈现出冬季最大, 春秋次之, 夏季最小的特点. 其次, 利用全球气候模式研究了不同的Lcf*值对模拟的云辐射强迫的影响, 研究结果表明, 不同Lcf*取值对模拟的云辐射强迫有很大影响, 特别是对全球几个主要的季风区和中东太平洋地区的影响非常大, 最高达40～50 W m-2左右. 因此, 在气候模式中精确描述云的垂直重叠结构对提高云辐射强迫模拟精度及其反馈有重要的意义.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".