Temperate Silvopasture Tree Establishment and Growth as Influenced by Forage Species and Cultural Management Practices
Bibliographic record
Abstract
Tall fescue (Lolium arundinaceum (Schreb.) Darbysh.) is known to inhibit tree growth. Competition for moisture and nutrients, and possibly allelopathy are suspected. This study examined if tall fescue inhibits tree growth more than two other cool-season forages, if tree growth differences are attributed to forage yield or tall fescue’s endophyte, and if irrigation and fertilizer can alleviate forage inhibition of tree growth. Four silvopasture tree species were planted into sods of three cool season forage species and grown four years with and without irrigation and fertilizer. Differences in tree growth did not correspond with forage dry matter yield or tall fescue endophyte status. Black walnut and red oak height and diameter growth and pitch x loblolly pine diameter growth were greater in Kentucky bluegrass and orchardgrass compared to the forage tall fescues [Ky-31 (E+), Ky-31 (E-), and Max-Q]. Irrigation and/or fertilizer did not alleviate forage competition on two tree species.
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.
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) | 0.001 | 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".