Nutrient ratios, foliar vector analysis, and nutrient use efficiency of four conifer stands growing under contrasting competing vegetation control treatments in the Pacific Northwest of the United States
Bibliographic record
Abstract
This study investigates competing vegetation effects on foliar and total plant-derived nutrient ratios, nutrient use efficiency (NUE), and foliar nutrient content and concentration of ecosystem components using vector analysis for 19-year-old Douglas-fir ( Pseudotsuga menzeisii (Mirb.) Franco), western hemlock ( Tsuga hereophylla (Raf.) Sarg.), western redcedar ( Thuja plicata Donn ex D. Don), and grand fir ( Abies grandis (Dougl.) Lindl.) stands in Oregon's Coast Range and for Douglas-fir and western redcedar in Oregon's Cascade foothills. Treatments included the Control, which received no spring release herbicide applications, and vegetation management (VM), which received 5 years of spring release herbicide applications, reducing competing vegetation abundance. VM increased the NUE of N, P, Mg, S, and Cu across all species when calculated with total plant-derived carbon and of all nutrients when calculated with stemwood carbon. VM often produced more harvestable and plant-derived carbon per unit nutrient fixed, improving the NUE of stands managed for carbon sequestration and timber. Species showed different stand nutrient requirements, evident through foliar and plant-derived nutrient ratios and their relationship with biomass production. Grand fir may obtain larger biomass increments for a given P:N ratio in plant-derived tissue and may be efficient in P-limited Coast Range sites.
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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".