Long-term productivity of production-scale, high-diversity prairie biomass feedstocks
Bibliographic record
Abstract
Concerns over climate change and resource usage have increased interest in the use of prairie biomass as a form of alternative energy. In this study, we examined productivity and weed resistance in four potential prairie biomass feedstocks with varying diversity (a switchgrass monoculture; a 5-species mixture of C4 grasses; a 16-species mixture of C4 grasses, forbs and legumes; and a 32-species mixture of C4 grasses, C3 grasses, forbs, and legumes) over a 10-year period. Each feedstock was specifically designed for high productivity. Four replicate production-scale (0.33–0.56 ha) plots were planted of each feedstock on three soil types. Productivity was assessed by hand-harvesting vegetation each year. Feedstocks had similar productivity over the study period (6.4–7.5 Mg·ha−1·year−1); however, the relative ranking of the four feedstocks differed across soil types. The 32-species feedstock had the highest interannual variation in biomass production. The switchgrass monoculture had the highest percentage of weed (unseeded species) biomass, and this percentage increased dramatically over the study period. The results indicate that high-diversity prairie biomass feedstocks are productive across a range of soil types but species composition should be specifically tailored to site conditions to maximize long-term productivity and resistance to weed invasion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".