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Record W4401495928 · doi:10.5376/ijmec.2024.14.0005

Relationship Between Genetic Diversity and Habitat Preference: A Case Study of Butterflies (<i>Rhopalocera</i>)

2024· article· en· W4401495928 on OpenAlexvenueno aff
Jun Wang, Qibing Xu

Bibliographic record

VenueInternational Journal of Molecular Ecology and Conservation · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsPreferenceGenetic diversityHabitatDiversity (politics)BiologyEcologyZoologyGeographyMathematicsStatisticsDemographySociologyAnthropologyPopulation

Abstract

fetched live from OpenAlex

This study analyzes the relationship between genetic diversity and habitat preference in butterflies ( Rhopalocera ). It systematically introduces the diversity of butterflies, including their extensive classification and ecological distribution, as well as their life cycle and behavioral characteristics. The concepts of genetic diversity and habitat preference are further explored, emphasizing their importance for species survival and adaptability. By reviewing existing research, including the application of genomic technologies in butterfly studies and the factors influencing butterfly habitat preferences, the study delves into the association between butterfly genetic diversity and habitat preference, highlighting differences among various butterfly species and possible mechanisms. This research provides scientific evidence and future research directions for understanding the relationship between butterfly ecology and genetics.

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 score0.030
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.062
GPT teacher head0.246
Teacher spread0.184 · 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
Published2024
Admission routes1
Has abstractyes

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