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Record W2133746927 · doi:10.1139/x08-156

The effect of nursery substrate and fertilization on the growth and ectomycorrhizal status of containerized and outplanted seedlings of <i>Picea abies</i>

2009· article· en· W2133746927 on OpenAlexvenueno aff
Lu‐Min Vaario, A. Tervonen, Kati Haukioja, Markku Haukioja, Taina Pennanen, Sari Timonen

Bibliographic record

VenueCanadian Journal of Forest Research · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsnot available
Fundersnot available
KeywordsPicea abiesShootBiologyHorticultureBotanySubstrate (aquarium)Human fertilizationBark (sound)FertilizerEctomycorrhizaPeatAgronomyMycorrhizaEcologySymbiosisBacteria

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.015
GPT teacher head0.258
Teacher spread0.242 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations48
Published2009
Admission routes1
Has abstractyes

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