An Ecosystem Approach to Wild Rice-Fish Cultivation
Bibliographic record
Abstract
Naturally grown wild rice (Zizania sp.) in freshwater lakes and streams with suitable biophysical conditions could provide opportunities for fish cultivation in different parts of the world, including North America. Many fish species prefer wild rice ecosystems for their habitat. Such natural aggregation could inspire wild rice-fish cultivation. Wild rice-fish integration could play a major role in maintaining ecosystems, including aeration of water, pest control, photosynthesis, nutrient cycling, respiration, soil fertility, and water quality. Wild rice-fish cultivation would be an ecosystem approach due to the positive culture attributes in terms of environmental benefits. Human consumption of wild rice and fish would provide a complementary, healthy, nutritious, and low-fat diet, with rich in carbohydrate, protein, minerals, and vitamins. Ideally, wild rice-fish integration could provide a wide range of social, economic, and ecological advantages, including food production, human nutrition, livelihoods, income, biodiversity conservation, and ecosystem services. Despite opportunities and potential benefits in North America, wild rice-fish culture has not yet been practiced. Empirical research with key stakeholders’ involvement need to address social, economic, and ecological challenges for wild rice-fish cultivation to increase food productivity and environmental sustainability.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".