Optimizing nitrogen loading of<i>Picea mariana</i>seedlings during nursery culture
Bibliographic record
Abstract
Plant growth and nutrient uptake response to increased fertilization can be conceptually described by cur vi linear relationships depicting phases of nutrient deficiency, sufficiency, luxury consumption, and toxicity to rationalize fertilizer prescriptions and improve nutrient diagnosis. We validated this model to determine optimum nitrogen storage of young black spruce (Picea mariana (Mill.) BSP). Container seedlings were supplied with a mixed nitrogenphosphoruspotassium (NPK) fertilizer at rates ranging from 0 to 80 mg N/seedling and reared in a greenhouse for one growing season. Plant growth and nutritional parameters of the plants exhibited classic responses of N deficiency, luxury consumption, and toxicity that were corroborated by vector diagnosis and appeared consistent with the conceptual model. Seedling biomass production was maximized at sufficiency (30 mg N/seedling), whereas N content of tissues peaked at the optimum loading rate (64 mg N/seedling). Toxicity occurred at the 80 mg N/seedling dose rate that increased plant N concentration (5%) but reduced growth (17%) and N content (14%) relative to the optimum level. Plant N content was raised 150% by optimum loading, exemplifying the effectiveness of this practice for building internal N reserves prior to planting. The newly validated model will help refine fertilizer recommendations and nutrient diagnosis for other species or cultural systems.
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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".