Temporal Variability in Fine Carbonaceous Aerosol over Two Years in Two Megacities： Beijing and Toronto
Bibliographic record
Abstract
在北京和多伦多的好喷雾器粒子(PM2.5 ) 的集中与相比的每周全部的碳(TC ) 的时间系列通过 2003 年 7 月从 2001 年 8 月在二年调查他们的各自的层次和时间的模式。除了这比较，在贡献观察集中和他们的时间的变化的因素的差别被讨论。与高度对比空气污染物质层次在二个大城市的过去的知识之上基于，这不令人吃惊在北京的平均 TC 集中(31.5 渭 g C m ? 3 ) 在由 8.3 的一个因素的多伦多比那大。尽管有他们的大集中差别，在两个城市里， TC 包括了 PM2.5 的一个同样大的部件。TC 集中展出了在二个城市之间的很不同的季节的模式。在北京，而在多伦多这行为在夏天被看见， TC 在冬季经历了高级、更大的每周的变化。作为结果，在在北京和多伦多之间的 TC 集中的最大的差距(由 12.7 的一个因素) 发生在冬季，当最小的差距(4.6 的一个因素) 在夏天时。在北京，，在排出物的季节的变化可能在影响 TC seasonality 比气象学起了一个更大的作用在多伦多在温暖的月期间中的超过 80% 个时时，风从南方被记录，与为有高 TC 集中的日子的许多潜在的人为的来源一起。差别的这比较提供卓见进在每个城市里影响碳的喷雾器的主要因素。关键词总数碳 - PM2.5 - 时间的变化 - 碳的喷雾器 - 北京 - 多伦多
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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 | 0.001 |
| 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 | 0.001 | 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".