Effects of low temperature and nitrogen addition during cold stratification on seed germination of Korean pine (<i>Pinus koraiensis</i>)
Bibliographic record
Abstract
Pinus koraiensis Siebold & Zucc. seeds are primarily dormant following dispersal, restricting the natural regeneration of populations. The aim of this study was to determine the combined effects of increasing low temperature and nitrogen addition during cold stratification on seed germination. We stratified the seeds at 0 °C, 5 °C, and 10 °C for 3.5 months. At each cold stratification temperature, seeds were subjected to 0 (0 nitrogen), 0.08 (low nitrogen), 0.16 (medium nitrogen), and 0.25 mol·L–1 (high nitrogen) nitrate additions. Seed germination response was tested at 25/15 °C and 10/5 °C. Under 0 nitrogen and high nitrogen levels, increasing cold stratification temperature did not markedly affect seed germination at 25/15 °C, but significantly inhibited seed germination at 10/5 °C. At medium nitrogen levels, seed germination at both 25/15 °C and 10/5 °C significantly decreased. At low nitrogen levels, increasing cold stratification temperature significantly decreased germination percentage at 25/15 °C from 86.7% (0 °C) to 50.2% (10 °C) but did not markedly change seed germination percentage at 10/5 °C. We suggest that the effect of increasing cold stratification temperature on seed germination of P. koraiensis depends on both the level of nitrogen addition during cold stratification and subsequent incubation temperature after cold stratification.
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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".