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Record W6922003562 · doi:10.1139/cjps-2014-340

Species-specific fertilization can benefit container nursery crop production

2015· article· en· W6922003562 on OpenAlexaboutno aff

Bibliographic record

VenueBioOne Complete (BioOne) · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicSeedling growth and survival studies
Canadian institutionsnot available
Fundersnot available
KeywordsFertilizerHuman fertilizationCanopyCropShrubDry weight

Abstract

fetched live from OpenAlex

Clark, M. J. and Zheng, Y. 2015. Species-specific fertilization can benefit container nursery crop production. Can. J. Plant Sci. 95: 251-262. To determine the responses of six container-grown shrub species to different controlled-release fertilizer (CRF) application rates, plant growth and root-zone traits were evaluated following fertilization with Polyon® 16-6-13, 5-6 month CRF incorporated at 0.60, 0.89, 1.19, 1.49 and 1.79 kg m-3 N. The six species tested at a southwestern Ontario, Canada, nursery were Cornus stolonifera ‘Flaviramea’ (yellow-twig dogwood), Euonymus alatus ‘Compactus’ (dwarf winged euonymus), Hydrangea paniculata ‘Grandiflora’ (Pee Gee hydrangea), Physocarpus opulifolius ‘Nugget’ (Nugget ninebark), Spiraea japonica ‘Magic Carpet’ (Magic Carpet spirea), Weigela florida ‘Alexandra’ (Wine and Roses weigela). Different species responded differently to the CRF rates applied. For the majority of species at the final harvest, growth index, plant height, canopy area, leaf area and above-ground dry weight were greater in high vs. low CRF rates; however, different species had different optimal CRF application rates or ranges: 1.49 kg m-3 N for Hydrangea and Spiraea, 1.19 kg m-3 N for Weigela, 1.19 to 1.49 kg m-3 N for Cornus and Physocarpus, and =0.60 kg m-3 N for Euonymus. Based on these species-specific optimal fertilizer rates or ranges, growers can group plant species with similar fertilizer demands, thereby reducing fertilizer waste and maximizing plant production.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.224
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.366
GPT teacher head0.238
Teacher spread0.128 · 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.

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

Citations0
Published2015
Admission routes1
Has abstractyes

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