Exponential fertilization of <i>Pinus monticola</i> seedlings: nutrient uptake efficiency, leaching fractions, and early outplanting performance
Bibliographic record
Abstract
We evaluated nutrient uptake efficiency and subsequent leaching fractions for western white pine (Pinus monticola Dougl. ex D. Don) seedlings grown with exponentially increasing or conventional (constant) fertilization in a greenhouse. Conventional fertilization was associated with higher leachate electrical conductivity and greater nutrient losses, which were more pronounced for the more mobile NO3-N than for NH4-N. Exponential fertilization increased seedling nitrogen (N) uptake efficiency (75%), which helped reduce leaching losses compared to conventional fertilization (50%). Although exponentially fertilized plants received 45% less fertilizer (20 mg N·plant1) during the establishment and accelerated growth phases compared with conventional cohorts (36 mg N·plant1), seedling morphological characteristics and nutrient status were similar at the end of greenhouse culture and after 2 years of growth in the field, except that exponential fertilization increased root volume after the first year and also increased ectomycorrhizal colonization. Reduction of applied fertilizer quantities and enhanced fertilizer uptake efficiency through exponential fertilization allows for production of high-quality seedlings while simultaneously minimizing fertilizer inputs and mitigating potential environmental contamination.
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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) | 0.001 | 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".