Late-season fertilization of Picea mariana seedlings: intensive loading and outplanting response on greenhouse bioassays
Bibliographic record
Abstract
Traditional greenhouse culture involves a late-season hardening period that withholds irrigation and fertilization from black spruce seedlings to promote frost-hardiness. Since nutrient uptake is limited without supplemental fertilization, growth during hardening may lead to internal nutrient dilution, a condition detrimental to field performance of seedlings. We examine whether late-season fertilization, applied as intensive loading, will counter dilution and build up nutrient reserves in seedlings reared conventionally or nutrient loaded before hardening. A mixed NPK fertilizer delivering 0, 12, 24, or, 48 mg N·seedling–1 for 9 weeks after bud set was tested. Root and shoot dry mass increased as much as 104 and 42% during hardening. Seedling biomass, however, was unchanged by late-season fertilization, but N uptake was increased 44–167% signifying induced luxury consumption. Extra K supplementation of treatments averted K dilution in plant tissues often occurring with high N addition. A 13-week outplanting trial on intact soil bioassays retrieved from a boreal site showed that growth and nutrient allocation were significantly enhanced by larger N reserves built up after intensive nutrient loading. About 72–80% of N required for new shoot growth was met from internal cycling, demonstrating the capacity of loading to enhance retranslocation. Intermediate loading (24 mg N) was most effective in promoting N accumulation and outplanting growth of both seedling types. Survival was reduced (30%) only at the highest dose. Study results demonstrate the potential advantage of these practices to improve growth of newly planted seedlings on northern forest 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) | 0.001 | 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 | 0.001 |
| 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".