Relative Rooting Depths of Native Grasses and Amenity Grasses with Potential for Use on Roadsides in New England
Bibliographic record
Abstract
Erosion is a significant problem on highway embankments in Rhode Island. At present, a mixture of red fescue ( Festuca rubra L.), perennial ryegrass ( Lolium perenne L.), and kentucky bluegrass ( Poa pratensis L.) is planted to stabilize the soil. However, only the red fescue survives long term on slopes. Red fescue is shallow-rooted, leading to sod sloughing after heavy rains. The objective of this study was to compare the rooting depth, plant height, and adaptation to roadside conditions of 16 native grasses and five amenity grasses with red fescue to identify species that could be used to reduce sod sloughing. Research was conducted from May 2006 through Aug. 2009 in the greenhouse at the University of Rhode Island in Kingston and on the shoulder of state Route 4 in North Kingstown, RI. The cool-season grasses smooth brome ( Bromus inermis Leyss. ssp. inermis ), Pumpelly's brome [ Bromus inermis Leyss. ssp. pumpellianus (Scribn.) Wagnon], Canadian wildrye ( Elymus canadensis L.), Virginia wildrye ( Elymus virginicus L.), silky wildrye ( Elymus villosus Muhl. ex Wild.), eastern bottlebrush ( Elymus hystrix L.), perennial ryegrass, and tall fescue ( Festuca arundinacea L.) and the warm-season grasses little bluestem [ Schizachyrium scoparium (Michx.) Nash] and purple lovegrass [ Eragrostis spectabilis (Pursh.) Steud.] were similar in height to red fescue while rooting significantly more deeply. Of these 10 species, little bluestem, purple lovegrass, and tall fescue showed the best survival on the roadside.
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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) | 0.002 | 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".