Facilitation in Rare Plants: Weaving knowledge systems to build healthy plant communities
Bibliographic record
Abstract
Rare plants have been found to exist in communities that have plants that facilitate or help one another succeed. However, to the detriment of biodiversity, the mechanisms of facilitation are understudied. To address this knowledge gap, studies in ecology are increasingly recognizing and encouraging collaboration with First Nations communities and the use of Indigenous science to inform restoration of rare plant ecosystems. The challenge now is successfully weave western science and Indigenous Science. This interdisciplinary project addresses this gap by braiding western science and Indigenous Science in collaboration with both Api’soomaahka, an Nitsitaapi elder, and University of Calgary experts. Further, the project uses collaboration to understand the facilitative interactions of two rare species in southern Alberta: Green Milkweed, and Purple Geranium. The authors hosted four ‘Plant Walks’, a collaborative field survey technique. ‘Plant walks’ facilitated knowledge exchange and provided an opportunity to share Nitsitaapi protocol through land-based learning. Preliminary conclusions include learnings from community-based and interdisciplinary research and implementation of knowledge systems to inform how we do field work on traditional Indigenous land.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".