Fungal Amalgamation for Enhanced Soil Fertility in Northern Ontario: A Community-Based Strategy for Indigenous Communities’ Food Security
Bibliographic record
Abstract
This proposal explores the potential of fungal amalgamation and Community-Based Participatory Research (CBPR) in addressing food insecurity among Indigenous communities in Northern Ontario.1 For Indigenous Peoples, cultivating and sustaining traditional food systems is deeply intertwined with cultural identity, a reciprocal relationship nurturing the land and community.2 However, forced relocations in the 1950s severed many communities from their ancestral lands, often moving them to areas with poor soil quality and harsh climatic conditions. In Northern Ontario, much of the land consists of fibrisol soils, which are highly acidic, nutrient-deficient, and prone to waterlogging; this creates significant barriers to farming. With limited access to arable land, many Indigenous communities rely on expensive, low-quality imported food, highlighting the need for locally driven solutions.3 Fungi, as primary decomposers, break down organic matter and facilitate nutrient cycling, making them a key factor in restoring soil fertility..4 This study adopts a two-pronged approach: utilizing CBPR to develop a culturally informed understanding of food insecurity and exploring fungi to solve this poor agricultural yield in sub-arctic communities. Using mycology as an innovative strategy, this research investigates the potential of fungal inoculation to enhance soil quality, crop resilience, and overall agricultural sustainability. A controlled greenhouse study will introduce fungal spores from psychrophilic, arbuscular mycorrhizal, and saprophytic fungi into fibrisol soil to assess their impact on crop yield, drought tolerance, and soil structure. Preliminary findings from greenhouse trials are expected to demonstrate the effectiveness of fungal inoculation in enhancing soil viability. These insights can inform larger-scale agricultural initiatives tailored to the unique needs of Indigenous communities. Furthermore, this research reinforces the importance of reconnecting Indigenous communities' relationship with their land. Integrating mycology into food security strategies offers a culturally relevant contribution to ongoing reconciliation efforts: supporting Indigenous food sovereignty whilst advocating for sustainable, community-led practices.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".