Seedling Growth Responses to Nutrient and Water Treatments Among Jack Pine Open-Pollinated Families
Bibliographic record
Abstract
Our study, conducted in a controlled greenhouse environment over a single growing season, evaluated the growth of seedlings from 25 open-pollinated families of jack pine (Pinus banksiana Lamb.) under two nutrient levels (20 ppm and 200 ppm) and three water regimes (twice a week, once a week, and once every two weeks). We assessed the effects of seed weight, family, nutrient availability, and water treatments on several growth parameters, including height, root collar diameter, shoot dry biomass, root dry biomass, total dry biomass, growing period length, and shoot-to-root ratio at harvest. We found that seed weight significantly influenced all growth traits, maintaining its effect throughout the growth season, although its impact diminished over time. Jack pine families were more responsive to nutrient treatments than to variation in water availability. Genetic variation was significant for all traits except the shoot-to-root ratio, highlighting the intricate role of genetic makeup in shaping growth responses. The substantial impact of nutrient and water treatments and relatively low heritability estimates suggest that pre-conditioning seedlings through nursery management can optimize shoot-to-root ratios. The minimal family-by-treatment interaction and the consistent performance of families across treatments suggest the potential for selecting high-efficiency genotypes with enhanced nutrient use efficiency and drought tolerance.
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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".