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Genetic diversity and population structure of Drosophila melanogaster in diverse ecological niches

2025· article· en· W4410379728 on OpenAlexaff
Vundela Swathi, Shivangi Gupta, Ravi Kumar, Abhishek Singla, S.M. Indumathi

Bibliographic record

VenueJournal of Entomological Research · 2025
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicInsect behavior and control techniques
Canadian institutionsImpact
Fundersnot available
KeywordsEcological nicheDiversity (politics)Drosophila melanogasterPopulationGenetic diversityEvolutionary biologyNicheEcologyBiologyDrosophila (subgenus)MedicineGeneticsGeneSociologyEnvironmental healthAnthropology

Abstract

fetched live from OpenAlex

AbstractPresent study explores how environmental factors, population dynamics, and evolutionary processes shape the genetic diversity of Drosophila with a focus on different natural and urban habitats. We outline the significance of studying Drosophila in its natural environment and the evolutionary mechanisms that maintain genetic variation. The case studies and genetic analyses showcasing the variability of genetic markers (e.g., microsatellites, SNPs) and population structures of Drosophila from varied ecological niches. We examine gene flow, genetic drift, and natural selection as key drivers of population divergence and structure and our findings highlights the adaptive potential of D. melanogaster to diverse ecological conditions and the relevance of these findings to broader evolutionary studies. As human activities and climate change continue to alter ecological landscapes, understanding how genetic diversity and population structure respond is critical for both conservation and evolutionary biology.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.067
GPT teacher head0.342
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), 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
Published2025
Admission routes1
Has abstractyes

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