Evaluation of Grass and Legume Species as Perennial Ground Covers in Corn Production
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Corn ( Zea mays L.) stover has been identified as an important feedstock for biofuel production but its removal will likely increase soil erosion. To address this issue 35 species of grasses and legumes were evaluated as potential perennial ground covers (PGCs) in corn. Selection of species encompassed both C3 and C4 species with a wide range of developmental and morphological features. The objectives were to (i) identify species that could support a high level of corn production while requiring minimal management and (ii) identify morphological traits and growth habits of suitable entries as PGC. Over the 3‐yr study period species with slow growing and spreading habits were more conducive to corn production, even though these PGCs still caused an average 23% reduction in corn grain yield. Meadow fescue ( Festuca pratensis Huds.), sheep fescue ( Festuca ovina L.), Canada bluegrass ( Poa compressa L.), fowl bluegrass ( Poa palustris L.), and colonial bentgrass ( Agrostis capillaris L.) were identified as suitable PGC species. These species were generally shorter and slower to spread into the corn rows compared with other, more aggressive species. Based on these observations an ideotype for future PGC species should be low growing, clump forming, and shade tolerant and have delayed green‐up in the spring.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it