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Record W2164812621 · doi:10.21273/hortsci.43.2.494

Growing Black Chokeberry (Aronia melanocarpa) in Cut-over Peatlands

2008· article· en· W2164812621 on OpenAlexafffundabout
Julie Bussières, Stephanie A. Boudreau, G. Clément-Mathieu, Blanche Dansereau, Line Rochefort

Bibliographic record

VenueHortScience · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPeatFertilizerVegetative reproductionMulchHorticulturePlant growthAgronomyHuman fertilizationYield (engineering)ChemistryBotanyBiologyMaterials science

Abstract

fetched live from OpenAlex

This project was established to evaluate the feasibility of black chokeberry [ Aronia melanocarpa (Michx.) Ell.] culture on Canadian cut-over peatlands and to define its appropriate production practices. We tested the effects of different fertilizer rates, application methods, and mulches on the vegetative development and berry production of seedlings over a 6-year period (2000 to 2006). Fertilization was essential for survival of the plants. A low fertilizer rate (131 g/plant of 1.7N–4.1P–12K) ensured adequate vegetative development for good establishment. Applying such a rate for 2 consecutive years proved to be more beneficial than a single dose of fertilizer for increasing plant height, crown width, and height growth. However, fruit yield was best when fertilized with the highest dose tested (550 g/plant of 3.4N–8.3P–24.2K). Black plastic mulch had a positive effect on height, width, and growth of the plants. More weeds were observed when the fertilizer was applied to the surface as compared with soil incorporation. However, this study revealed that black chokeberry is tolerant of weeds. Based on these results, black chokeberry is a promising plant for the management of cut-over peatlands for both vegetative growth and fruit 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 categoriesInsufficient 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 score0.041
Threshold uncertainty score0.999

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 studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.220
Teacher spread0.209 · 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

Citations15
Published2008
Admission routes3
Has abstractyes

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