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Record W4386060155 · doi:10.23910/2/2022.ijep0490

Morphological Evaluation and Selection of Gladiolus (Gladiolus×Hybridus L.) Hybrids for Commercial Traits

2022· article· en· W4386060155 on OpenAlexaboutno aff
Kishan Swaroop, Kanwar Pal Singh, Arun Kumar, R. L. Misra

Bibliographic record

VenueInternational Journal of Economic Plants · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFlowering Plant Growth and Cultivation
Canadian institutionsnot available
FundersIndian Agricultural Research InstituteIndian Council of Agricultural Research
KeywordsGladiolusHybridBiologySeedlingHorticultureCormFloricultureOrange (colour)Botany

Abstract

fetched live from OpenAlex

This experiment was conducted with twenty-five gladiolus hybrids along with a check at the research farm of the Division of Floriculture & Landscaping, ICAR, Indian Agricultural Research Institute, New Delhi to study the performance and suitability of hybrids for different traits. The mean performance of gladiolus hybrid data was highly significant for all the characters studied; however, the results indicated that early flowering was seen in six hybrids such as Smokey Lady×Heady Wine Open seedling, The Berton Open seedling, Green Willow× Oscar, Shweta×Regency, Canada×Green Finch and Howard×Rose Time Steamboat and ranging from 82.33 to 86.00 days after planting. The maximum plant height 127.66 cm, spike length 116.00 cm and rachis length 63.33 cm were observed in Rose Time Steamboat hybrid, but number of florets per plant 19.33 was recorded in Canada×Green Finch hybrid. The number of corms i.e., three or more than three were recorded in seven hybrids namely; Oscar×Green Willow, (Snow Princess×Ratna)×Urmil, Smokey Lady×Mayur, Snow Princess.×Howard, Berlew Open seedling (Dark orange), Rose Time Steamboat Open seedling and Pink Parassol Open seedling respectively; whereas number of cormels in the range of 50.00 – 64.00 were recorded in five hybrids including check variety.

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 score0.863
Threshold uncertainty score0.393

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 head0.030
GPT teacher head0.259
Teacher spread0.230 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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