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Record W2084856249 · doi:10.1139/x04-088

Pretransplant fertilization of containerized<i>Picea mariana</i>seedlings: calibration and bioassay growth response

2004· article· en· W2084856249 on OpenAlexfundvenueno aff
V. R. Timmer, Yue Teng

Bibliographic record

VenueCanadian Journal of Forest Research · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBlack spruceNutrientBioassayTransplantingHuman fertilizationSowingGrowing seasonBiologyAgronomyBotanyHorticultureTaigaAnimal scienceEcology

Abstract

fetched live from OpenAlex

The role of the root plug as a nutrient source for newly planted seedlings was evaluated for one growing season on soil bioassays retrieved from a boreal forest site. Intact (control) and bare-rooted (peat plug removed) black spruce (Picea mariana (Mill.) B.S.P.) seedlings reared in Jiffy pellets, some fertilized before ("spiked" with 60 mg N) or after (topdressed with 300 mg N) planting, were transplanted to potted soil blocks (bioassays) under greenhouse conditions. Compared with the intact control, bare-rooting alone reduced plant dry mass (16%) and N, P, and K (15%–25%) uptake, but increased these parameters (62%–101%) when combined with topdressing, suggesting that the root plug served as a crucial nutrient reserve soon after transplanting. Nutrient spiking or topdressing alone stimulated growth and nutrient uptake as well (35%–118%), but generated the largest response (81%–205%) when applied together. Mortality (7%–18%) occurred only with bare-rooting treatments. The responses reflected the sensitivity of seedlings to nutrient supply changes both in root plugs and in field soils. Nutrient spiking was more efficient in improving seedling performance than traditional topdressing because of reduced fertilizer requirements and closer availability of added nutrients for early root development.

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)0.002
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 score0.094
Threshold uncertainty score0.958

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.025
GPT teacher head0.261
Teacher spread0.236 · 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

Citations16
Published2004
Admission routes2
Has abstractyes

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