Effects of Root-trimming and Cutting-heights on Growth Performance of Potted Native Warm-season Grasses
Bibliographic record
Abstract
Mechanized transplanting of native warm-season grass (NWSG) seedlings raised in biodegradable strip-cups may require trimming outgrown and entwined roots to facilitate individual placement and complete root covering. During establishment, mowing is often used to reduce weed competition and promote tillering. In two randomized complete block split-split-plot design experiments, effects of root-trimming and cutting-height on growth and biomass production of potted NWSGs [big bluestem (BB, Andropogon gerardii Vitman), eastern gamagrass (GG, Tripsacum dactyloides L.), indiangrass (IG, Sorghastrum nutans L.), and switchgrass (SG, Panicum virgatum L.)] were assessed. Six-week old seedlings were transplanted, with or without root-trimming, and four of each type and species, assigned to 10-, 15-, or 20-cm cutting-height. All plants were fertilized uniformly and watered sufficiently. After a 7-d adjustment period, plants were clipped to 10 cm which promoted tillering. A three-week regrowth was then allowed before the first of three forage harvests, at assigned cutting-heights. Plant heights were recorded every two weeks after transplanting and on each harvest date. Data were analyzed for effects of root-trimming, cutting-height, and species. Root-trimming had no effect on the parameters. Cutting-height had no effect on plant heights except for second GG and SG regrowths, and/or the third BB and SG. Cutting-height affected only SG forage biomass significantly (P < 0.05) during year1 and every species during year2 with 100%+ greater values at the 20- than the 10-cm. All 20-cm average growth rates and belowground biomass in year2 were greater (P < 0.001) than the 10-cm by > 100%, but with similar root:total biomass ratios. Overall, species yield increased in the order; IG
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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) | 0.001 | 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 | 0.001 |
| 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 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".