Effects of wild rice (<i>Zizania palustris</i>) straw on biomass and seed production in northern Minnesota
Bibliographic record
Abstract
Wild rice ( Zizania palustris L.) litter accumulation may inhibit plant growth and production both by physically suppressing seedling emergence and by nitrogen immobilization in fresh litter. This latter mechanism could reduce nitrogen availability to plants early in the growing season at a period when more than half of the annual uptake occurs. To test the importance of these mechanisms, we planted wild rice in mesocosms. Half the tanks were planted with seeds sown below the litter and half were planted with seedlings grown to a height taller than litter thickness. One-third of the tanks were treated with fresh (nitrogen immobilizing) litter, one-third were treated with litter that had been incubated for 26 d and was mineralizing nitrogen, and one-third did not receive litter. These treatments resulted in a fully crossed factorial design, with nine replicates for each treatment combination, totaling 54 tanks. We measured plant growth, vegetative, root, and seed biomass, total plant N, and available N at 2.5 cm sediment depth. The presence of litter and its stage of decay caused plant, root, and seed biomass, and seed and total plant nitrogen content to increase. We found no physical inhibition of litter on the potential growth of plants started as seeds. Therefore, the timing of litter nitrogen immobilization or mineralization affects the potential growth of wild rice.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".