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Record W4392970027 · doi:10.9734/jabb/2024/v27i4745

Studies on Effect of IBA Concentrations and Planting Times on Propagation of Different Varieties of Rose (Rosa hybrida Vill.)

2024· article· en· W4392970027 on OpenAlexaff
J. S. Gupta, Jagseer Singh Brar, RK Dubey

Bibliographic record

VenueJournal of Advances in Biology & Biotechnology · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFlowering Plant Growth and Cultivation
Canadian institutionsCoquitlam College
Fundersnot available
KeywordsRose (mathematics)SowingHorticultureBiology

Abstract

fetched live from OpenAlex

The conventional method of rose propagation involves budding on one year old field established rootstock plants. For multiplication of plants, cutting is the simplest way of propagation of desirable rose varieties. There are relatively few varieties that develop roots, so exogenous application of growth promoters is essential. In order to study the effects of different concentrations of IBA (0, 500, 1000 and 1500 ppm) on rooting of different varieties (English Holiday, Eiffel Tower, Don Don, Confetti and Iceberg) of rose, during planting times viz., February, July, October and December, It was found that 500 ppm IBA resulted in maximum number of roots (15.66), number of leaves (22.33), the percentage of survival (50%), shoot length (13.23 cm), fresh weight of roots (4.80 g) and stem (5.93 g), dry weight of roots (4.36 g) and stem (4.33 g) and took minimum days (13.00) for leaf bud initiation, whereas,the maximum root length (7.80 cm) was found in 1000 ppm IBA in Eiffel Tower variety and control treatment had the lowest effect on the different traits of rooting in rose cutting . Among different varieties, Eiffel Tower was found to be best in terms of different traits, whereas English Holiday and Confetti didn't respond to any of the planting times and treatments. Cuttings planted in February showed the highest effect on their evaluated traits with a significant difference with those planted in July and October, whereas in December planting time, zero survival rates was observed.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.154

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.263
Teacher spread0.255 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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