Dereplications Can Amplify The Extent And Worth Of Traditional Pharmacopeias.
Bibliographic record
Abstract
As aculturization and globalization continues, there is an urgent need to carefully record and delineate traditional pharmacopeias so that their true worth is understood and protected and any possible benefits related to their commercial development are equitably distributed. In the past most of these endeavors resulted in a list of plants with their associated uses without providing further explanations as to the extent of this knowledge within the traditional group, or if this knowledge was known elsewhere. This practice tended to generate the notion of finite exclusivity without providing proof that this was actually the case. Moreover, since the talents and methods of those conducting these initial studies varied widely, little effort was made to provide adequate information on how selective processes and preferences as well as modes of collection, preparation and use were achieved. Without these data, the potential of their clinical worth, bioreactive capacities or chemical compositions were often compromised. This frequently led to expending much time, effort and treasure on a pharmacopeia's evaluation without guidance on how these efforts could be optimized to achieve its best possible medicinal potential. This paper will review how types of dereplications and other techniques are helpful in amplifying this process.
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".