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Record W2905037035 · doi:10.1139/cjps-2018-0151

Nutrient sufficiency levels for haskap (<i>Lonicera caerulea</i> L.) using the boundary-line approach

2018· article· en· W2905037035 on OpenAlexaffvenueabout
Ekene Mark‐Anthony Iheshiulo, Lord Abbey, Andrew M. Hammermeister

Bibliographic record

VenueCanadian Journal of Plant Science · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicBanana Cultivation and Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsNutrientNova scotiaBiologyLimitingHorticultureChlorophyllBotanyAgronomyEcology

Abstract

fetched live from OpenAlex

Adequate supply of plant nutrients is crucial for haskap plant growth and increased productivity. A study was carried out to determine the variability in haskap (Lonicera caerulea L. cv. Indigo Gem) plant characteristics in relation to soil and leaf tissue nutrient status. A total of 19 composite soil samples and corresponding plant leaf tissue samples were collected in 2016 from 12 locations in Nova Scotia. Plant parameters measured include growth rate, leaf size, leaf chlorophyll content, and visual observations. A boundary-line approach was used to determine nutrient sufficiency ranges in leaf tissues of 2.23%–2.96% for N, 0.22%–0.28% for P, 0.84%–1.32% for K, 1.63%–2.10% for Ca, and 0.14%–0.50% for Mg. Principal component and correlation analysis suggested a possible antagonistic interaction between leaf K and Mg. Negative associations were observed most frequently between Ca and Mg and other nutrients, especially K. Plant parameters such as bush volume, leaf size, and growth rate were closely related to soil and leaf K. Deficiencies in leaf tissue K and P were identified as potentially important factors limiting growth. Therefore, there is a need to adjust or balance the application of these nutrients. In conclusion, the sufficiency ranges derived can be used as a guiding principle in diagnosing the nutritional status of haskap cv. Indigo Gem on representative farms in Nova Scotia.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.106
GPT teacher head0.289
Teacher spread0.182 · 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 designBench or experimental
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

Citations10
Published2018
Admission routes3
Has abstractyes

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