Germination and propagation of sweetgrass ( <i>Anthoxanthum nitens</i> (Weber) Y. Schouten & Veldkamp [Poaceae]) for growing in Mi’kmaq community gardens
Bibliographic record
Abstract
Abstract Sweetgrass (Anthoxanthum nitens (Weber) Y. Schouten & Veldkamp [Poaceae]) is valued both ecologically and culturally by Mi’kmaq and other Indigenous groups. However, Mi’kmaq communities and organizations have expressed concern about sweetgrass becoming difficult for people to access due to loss of traditional knowledge, habitat degradation, climate change, and overharvesting. To help improve access and availability of sweetgrass, we assessed the viability and practicality of germinating native sweetgrass seeds for propagation and cultivation within Mi’kmaq communities with their guidance. To address our goal, we tested 1) seed storage length and temperature; 2) addition of biological additives to enhance germination; 3) methods for seed dormancy; and 4) the efficacy of outplanting seedlings into community gardens. We increased germination success up to 46% by removing seed coverings (bracts) and then soaking the seeds in water. Cold temperature storage, biological additives, and growth enhancers did not improve germination. As we learned through discussions with community members, leaf blade height, number of leaves, reproducibility, and scent are important sweetgrass characteristics for braiding and basketmaking. Bone and blood meal organic fertilizer (7-5-0) increased rhizome development for more plant growth, synthetic fertilizer (20-20-20) resulted in taller leaves, and both fertilizers increased the number of leaves. After one growing season, mature leaves from germinated sweetgrass in raised garden beds had similar or greater coumarin (scent) as their wild counterparts. Our work was the first study of sweetgrass propagation in collaboration with Mi’kmaq communities and serves to encourage the leadership of Indigenous communities to pursue sustainable and culturally respectful propagation of traditional medicines.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".