Indigenizing fungal biotechnology for planetary health: an opinion paper
Bibliographic record
Abstract
New fungal biotechnologies are advancing applied and conservation mycology to support global regenerative outcomes for natural and human systems. Here, we propose the Applied and Conservation Mycology Framework to align fungal biotechnology and Indigenous Knowledge Systems in support of planetary health, "the health of human civilization and the state of the natural systems it depends on." The Kunming-Montreal Global Biodiversity Framework (KM-GBF) adopted at the 2022 United Nations Biodiversity Conference is humanity's best effort at reconciling the sustainable development of all societies and biodiversity loss while reaffirming the rights of Indigenous Peoples (IPs). Through Indigenous Data Sovereignty (IDSov) and Governance (IDGov), fungal biotechnologies could help address all 23 KM-GBF Targets. In this opinion paper, we apply Indigenous relational science and knowledge systems to explore how advancements in fungal biotechnology and digital technology enable Indigenous Peoples to develop, practice, and govern fungal biotechnologies for applied and conservation mycology. We focus on the Kara & Kichwa Nations, Indigenous Peoples of Ecuador, the Cultural Mountain of Andea, and the Cultural Rainforest of Amazonia. The ACMF centers on fungal biotechnological innovation by Indigenous Peoples and their participation in the global bioeconomy in the service of planetary health and all 23 KM-GBF Targets. We offer a starting point for envisioning future fungal technologies developed by Indigenous Peoples and in service of planetary health.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".