Biopiracy of natural products and good bioprospecting practice
Bibliographic record
Abstract
BACKGROUND: Biopiracy mainly focuses on the use of biological resources and/or knowledge of indigenous tribes or communities without allowing them to share the revenues generated out of economic exploitation or other non-monetary incentives associated with the resource/knowledge. METHODS: Based on collaborations of scientists from five continents, we have created a communication platform to discuss not only scientific topics, but also more general issues with social relevance. This platform was termed 'PhytCancer -Phytotherapy to Fight Cancer' (www.phyt-cancer.uni-mainz.de). As a starting point, we have chosen the topic "biopiracy", since we feel this is of pragmatic significance for scientists working with medicinal plants. RESULTS: It was argued that the patenting of herbs or natural products by pharmaceutical corporations disregarded the ownership of the knowledge possessed by the indigenous communities on how these substances worked. Despite numerous court decisions in U.S.A. and Europe, several international treaties, (e.g. from United Nations, World Health Organization, World Trade Organization, the African Unity and others), sharing of a rational set of benefits amongst producers (mainly pharmaceutical companies) and indigenous communities is yet a distant reality. In this paper, we present an overview of the legal frameworks, discuss some exemplary cases of biopiracy and bioprospecting as excellent forms of utilization of natural resources. CONCLUSIONS: We suggest certain perspectives, by which we as scientists, may contribute towards prevention of biopiracy and also to foster the fair utilization of natural resources. We discuss ways, in which the interests of indigenous people especially from developing countries can be secured.
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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 | 0.062 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.082 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.012 | 0.008 |
| 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".