Growth of three oak species during establishment of an agroforestry practice for watershed protection
Bibliographic record
Abstract
Tree species that provide early benefits for farmers can serve to increase the adoption of temperate agroforestry practices. Growth of pin (Quercus palustris Muenchh.), swamp white (Quercus bicolor Willd.), and bur oak (Quercus macrocarpa Michx.) saplings was evaluated in an agroforestry practice designed for watershed protection in northern Missouri, USA, to identify species that provide early productive and protective functions. Large, containerized oak seedlings were planted in the center of 4.5-m-wide contour grass-legume strips established at 22.8- to 36.5-m intervals on a 4.44-ha watershed with a corn (Zea mays L.) soybean (Glycine max (L.) Merr.) rotation. Sapling survival, height, and diameter growth were annually recorded during five growing seasons. Branch and leaf mass were also recorded in the two last measuring years. Preliminary assessment of rooting pattern was made based on the relative abundance of horizontal and vertical main roots. Mean shoot growth rate of pin oak was the highest during the whole study period, while swamp white and bur oak grew at the same rate until the fourth growing season. During the fifth growing season, growth rate of bur oak was the lowest. The root system of pin oak appears to be concentrated close to the tree, thus strengthening the buffer strips and competing least with the crop. The shallow root system of swamp white oak may best scavenge leaching nutrients from subsurface flow, thus reducing nonpoint source pollution. Sapling survival, growth rate, and rooting pattern suggest that pin oak and swamp white oak have a better potential for agroforestry practices in the Midwest than bur oak.
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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) | 0.000 | 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".