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Record W2417663501 · doi:10.5539/jas.v8n7p163

Genetic Variability and Phylogenetic Relationship of Pakistani Snapmelon (Cucumis melo var. Momordica) by Using Microsatellite Markers

2016· article· en· W2417663501 on OpenAlexvenueno aff
Ghulam Rasool, Muhammad Jafar Jaskani, Amjad Ullakh, Rashid Ahmad

Bibliographic record

VenueJournal of Agricultural Science · 2016
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvances in Cucurbitaceae Research
Canadian institutionsnot available
Fundersnot available
KeywordsCucumisBiologyGenetic diversityMicrosatelliteGermplasmDendrogramGenetic relationshipFixation indexGenetic markerGenetic distanceGenetic variabilityDomesticationGenotypeBotanyGenetic variationVeterinary medicineHorticultureGeneticsPopulationAllele

Abstract

fetched live from OpenAlex

<p>Among the major cucurbit vegetables, <em>Cucumis melo </em>has one of the highest polymorphic fruit types and botanical varieties. The aim of the present study was to evaluate the genetic diversity among different genotypes of Snapmelon (<em>Cucumis melo</em> var. Momordica) collected from all the four provinces of Pakistan. In this study, 18 microsatellite markers were tested on Snapmelon germplasm which yielded valuable information about the genetic relationships among 40 Snapmelon accessions. The mean PIC value of the markers ranged from 0.3706 to 0.8247. For establishing data matrix, an auto radiogram was visually scored for the presence (1) or absence (0) of polymorphic bands. Assessments of genetic relationship among the genotypes were done by cluster analysis, using POPgen software. The genetic analysis through principle coordinate analysis (PCA) and dendrogram showed that the wild accessions were distinguished from all domesticated accessions collected from various regions of the country. Genetic differentiation among the populations using molecular data indicated the importance of the study area for species conservation, genetic erosion estimation, and exploitation in breeding programs.</p>

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.014
GPT teacher head0.292
Teacher spread0.278 · 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

Citations2
Published2016
Admission routes1
Has abstractyes

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