The effect of nursery substrate and fertilization on the growth and ectomycorrhizal status of containerized and outplanted seedlings of <i>Picea abies</i>
Bibliographic record
Abstract
Over a 5 year period, we examined the influence of substrate and fertilization on nursery growth and outplanting performance of Norway spruce (Picea abies (L.) Karst.). We focused on the relative growth and development of roots and shoots and the colonization intensity and diversity of ectomycorrhizal (ECM) fungi. In the nursery, a conventional substrate (low-humified Sphagnum peat) supplemented with woody material (wood fibre and pine bark) and either mineral or organic fertilizers yielded shorter seedlings than those grown on the unmodified substrate. However, after outplanting, the growth rate of seedlings cultivated on modified substrates was higher than that of seedlings grown on the unmodified substrate. Seedlings cultivated in modified substrates had significantly higher root/shoot ratios and ECM diversity; the latter remained significant after ≥3 years of outplanting. Seedlings grown on a substrate containing 50% woody material and supplemented with organic fertilizer had the highest growth rate among all seedlings during the 3 year period of outplanting. Colonization intensity of ECM fungi was high in all seedlings except for those grown in heavily fertilized substrate. This study suggests that nursery techniques that produce seedlings with higher root/shoot ratios and ECM diversities could improve plantation success and growth rate for at least the first 3 years of outplanting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".