Mining Indigenous Knowledge and Modern Science Simultaneously: A Novel Approach for Linking Human Knowledge with Pharmacological, Toxicological and Phytochemical Data
Bibliographic record
Abstract
Diabetes is a global health concern and a heavy burden on individuals and health care systems. Indigenous populations are particularly affected yet possess key knowledge in Traditional Medicine and local intervention strategies. Since 2003, we interviewed about 150 Cree Elders of Eeyou Istchee (Eastern James Bay area of Northern Quebec) and identified 17 Boreal forest medicinal plants species used traditionally against diabetes symptoms. A comprehensive data set was accumulated on these 17 plants that comprises not only Cree uses related to 15 diabetes symptoms, but also detailed pharmacological assessment using 55 cell-based and cell-free bioassays determining primary (susceptible to lead to blood glucose reductions) and secondary (including antioxidant, anti-inflammatory and diabetes complications) antidiabetic potential, as well as 14 toxicological bioassays (notably, cytochrome P450 assessments). The database also incorporates 465 unique chemical signals identified by HPLC-MS QTOF to circumscribe both common and novel secondary metabolites within these plants as well as biologically active compounds. Using multivariate analysis techniques to explore this data, preliminary results have identified clear trends that show distinct associations between the plant metabolomes and specific sets of the pharmacological data, with greater similarity among the plant parts tested than among plant families. With further development, this approach may prove useful to determine optimal treatment strategies or combinations of plants or their compounds for helping Cree diabetics manage their glycemic control in a culturally relevant manner. We can also eventually apply this approach to explore connections behind other diseases and the use of traditional medicine in other communities.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.021 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".