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Record W2055293815 · doi:10.4141/p03-107

Accumulation and distribution of micronutrients in Willamette red raspberry plants

2005· article· en· W2055293815 on OpenAlexvenueno aff
C. G. Kowalenko

Bibliographic record

VenueCanadian Journal of Plant Science · 2005
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBerry genetics and cultivation research
Canadian institutionsnot available
Fundersnot available
KeywordsMicronutrientBlowing a raspberryNutrientGrowing seasonRubusFertilizerCropBiologyHorticultureAgronomyChemistryEcology

Abstract

fetched live from OpenAlex

Willamette red raspberry (Rubus idaeus L.) plants were sampled monthly to determine the accumulation and distribution of copper (Cu), boron (B), zinc (Zn), manganese (Mn) and iron (Fe) over the growing seasons of four different years. The soil (Marble Hill series) was typical of that commonly used for commercial raspberry production in the area. No micronutrients were applied. The micronutrients generally accumulated as the season progressed in a manner similar to the accumulation of dry matter. Maximum accumulation of micronutrients in the aboveground portion of the plants did not necessarily occur at the end of the growing season because of different growth patterns of the floricanes and primocanes. The ripe berries, which would be removed by harvesting, contained a significant proportion of the total accumulation of micronutrients: Cu (18–26%), Zn (15–25%), B (3–16%), Fe (5–8%) and Mn (4–7%). However, concentrations of the micronutrients in the various parts of the plant varied considerably from month to month and year to year. There was no obvious relationship between weather conditions and the variations in micronutrient concentrations. Extreme variations within and between growing seasons suggest concentrations of micronutrients in leaves cannot readily be used to develop tissue-analysis-based fertility recommendations. Floricane stem micronutrient concentrations were more stable but the relationship between these concentrations and the overall nutrient status of the plants needs to be determined before they can be used for diagnostic purposes. Before accumulation and distribution patterns of micronutrients in raspberry can be used as a basis for development of fertilizer management strategies, additional measurements from other locations are needed. The results from this study should serve as guidelines for developing sampling procedures and their practical applications. Key words: Copper, boron, zinc, manganese, iron, Rubus idaeus L.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.254
Teacher spread0.218 · 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

Citations25
Published2005
Admission routes1
Has abstractyes

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