Interaction of vegetation control and fertilization on conifer species across the Pacific Northwest
Bibliographic record
Abstract
An experiment evaluating three levels of vegetation competition control (no control, 1.5 m2of vegetation control, and 3.3 m2of vegetation control), each with two fertilization treatments (fertilization at the time of planting with complete slow-release fertilizer (Woodace®IBDU), or no fertilization), was installed at five sites. Two of these sites were planted with Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) in the Oregon Coast Range, one with ponderosa pine (Pinus ponderosa Dougl. ex P. Laws. & C. Laws.) in eastern Washington, one with western hemlock (Tsuga heterophylla (Raf.) Sarg.) in the coastal hemlock zone in Oregon, and one with coastal redwood (Sequoia sempervirens (D. Don) Endl.) in northern California. At four of the five sites, mean stem volume, basal diameter, and height of seedlings increased significantly with increasing area of weed control, and the magnitude of difference between treatments increased with time. Fertilization significantly increased seedling size only at the two sites with adequate soil moisture; increases were marginally significant at a third. Response to fertilization was less than from weed control and impacted growth for only the first year, whereas the influence of weed control continued to influence growth the entire length of the study (4 years). Area of vegetation control and fertilization did not interact significantly at any site.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".