Establishment of HPLC-fingerprint analysis for the quality assessment of Angelica sinensis
Bibliographic record
Abstract
OBJECTIVE To establish the HPLC-fingerprint analysis for the quality control of Angelica sinensis .METHODS The HPLC method was used with Hypersil C_ 18 column(200 mm×4.6 mm,5 μm).The mobile phases were consisted of acetonitrile- 0.01% phosphoric acid (10∶90) and methanol-acetonitrile-water (10∶30∶60) respectively.The flow rate was 0.8 mL·min -1 .The UV detection was performed at 320 nm.36 batches of Angelica sinensis were determined.RESULTS The 36 batches of Angelica sinensis were classified to be qualified and unqualified based on the results of cluster analysis and similarity analysis.CONCLUSION The method was simple and reliable,and it was capable of effectively controlling the quality of Angelica sinensis .
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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 | 0.000 |
| 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 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".