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Record W2771048524 · doi:10.1139/cjps-2017-0039

Repetitive vegetative propagation of first-year sea buckthorn (<i>Hippophae rhamnoides</i> L.) cuttings

2017· article· en· W2771048524 on OpenAlexafffundvenue
Adam Dale, Dragan Galić

Bibliographic record

VenueCanadian Journal of Plant Science · 2017
Typearticle
Languageen
FieldMedicine
TopicPhytochemical and Pharmacological Studies
Canadian institutionsUniversity of Guelph
FundersAgricultural Adaptation Council
KeywordsCuttingHippophae rhamnoidesVegetative reproductionCultivarHorticultureBiologySowingGreenhouseBotany

Abstract

fetched live from OpenAlex

Sea buckthorn (Hippophae rhamnoides L.) is used in beverages, pharmaceuticals, cosmetic products, and animal feeds. Although sea buckthorn has been shown to be easy to propagate vegetatively, currently, there is little information on reliable techniques to vegetatively propagate the plant repetitively within a single year. To address this, three experiments were conducted to study whether season and chilling affected the successful rooting of cuttings. Four cultivars, ‘Chuskaya’, ‘Golden Rain’, ‘Lord’, and ‘Sunny’, were used in the season- and chilling-effect experiments. Hardwood and softwood cuttings from field-grown plants did not root from October to December. The percent of rooted cuttings in January was cultivar-dependent. The number and percent of rooted greenhouse-produced cuttings were significantly affected by length of chilling. Most cuttings were produced and the highest percent rooted when the plants chilled for at least 6 wk. ‘Lord’ had the most and ‘Golden Rain’ the least number of rooted cuttings. This study indicated that greenhouse-grown stock plants were a viable source of sea buckthorn cuttings for vegetative propagation. When combined with field-grown sources, it is possible to produce sea buckthorn planting material year-round.

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)0.002
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 score0.130
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.030
GPT teacher head0.289
Teacher spread0.259 · 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

Citations3
Published2017
Admission routes3
Has abstractyes

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