Lake Superior Manoomin cultural and ecosystem characterization study
Bibliographic record
Abstract
Manoomin, wild rice, is integral to the culture, livelihood, and identity of the Anishinaabeg, the indigenous peoples of Canada and the United States that include the Odawa, Ojibwe, Potawatomi, and Algonquin peoples. In addition to the vital role Manoomin has in the lives of the Anishinaabeg, Manoomin is recognized as being ecologically important, feeding migrating and resident wildlife species, providing a nursery for fish and nesting and breeding habitats for many waterfowl and muskrat, and stabilizing shorelines. This study was initiated by a team of Lake Superior basin Anishinaabe communities and federal and state agencies to document and characterize (1) the importance of Manoomin habitat to Anishinaabe cultural perspectives and identity, community connections, spiritual practices, food sovereignty, and food security; and (2) the ecological importance of Manoomin habitat as an indicator of a high-quality, high-functioning, and biodiverse ecosystem. The team applied a set of cultural and ecological metrics to characterize seven case study sites around Lake Superior and used a habitat equivalency analysis to determine the amount of restoration needed to counterbalance the lost Manoomin habitat functionality. Results from this study highlight the difficulty in restoring the cultural and ecological functionality of degraded Manoomin habitat and the importance of preserving and protecting existing Manoomin habitat.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".