Cluster analysis of flue-cured tobacco leaves from different growing areas according to chemical components correlating to aroma types.
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
Routine chemical components,metal ions,organic acids,polyphenols,and neutral aroma constituents in both domestic and imported flue-cured tobacco leaves were analyzed with aroma types by simple correlation in order to provide experimental basis for reasonable evaluation and better use of tobacco leaves from different growing areas.Results showed that in total 53 chemical indexes,22 were highly significantly or significantly correlated with aroma types.Cluster analysis was then conducted with these 22 chemical indexes.Results showed that when T=12.4,tobacco leaves from 18 regions were divided into 2 groups:one was those from abroad and Shaanxi Ankang of China,the other was from regions except Ankang.When T=5.6,tobacco leaves from 18 regions were divided into 7 groups,which in turn were from Zimbabwe and Canada,from Brazil and Shaanxi Ankang,from Fujian,Hubei Enshi,Chongqing Qianjiang,and Jiangxi Gannan,from Liaoning,Sichuan,and Yunnan,from Guangxi,Hunan,and Guizhou,from Henan and Shandong,from Heilongjiang Mudanjiang and Jilin.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it