The LOTUS initiative for knowledge sharing in Natural Products research
Bibliographic record
Abstract
With the recent explosion of information, Natural Products (NP) research critically needs efficient ways to access and share knowledge, also to save precious knowledge being lost [ 1 ]. The reporting and sharing of NP occurrences in biological organisms are relevant to numerous scientific fields ranging from drug discovery to chemical ecology or chemotaxonomy. Through the LOTUS initiative, we aim to offer better knowledge sharing in NP research. We established a data harmonization, curation, and validation pipeline to gather and appropriately document structure-organism pairs. These pairs are then shared through the Wikidata platform. Wikidata is particularly indicated for the sharing of knowledge in life sciences as demonstrated in [ 2 ]. We made 700,000+referenced structure-organism pairs available on Wikidata. As an example, it is, now possible to retrieve biological organisms containing chemical compounds described as anti-infective ( https://w.wiki/vo9 ). This offers exciting perspectives, linking information gained over different, sometimes disconnected, fields of investigation. Publication History Article published online: 13 December 2021 © 2021. Thieme. All rights reserved. Georg Thieme Verlag Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.020 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.019 |
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".