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Record W4400429171 · doi:10.5376/mgg.2024.15.0004

The Role of Isoenzymatic Variation in Delineating Phylogenetic Relationships within <i>Zea</i> Genus

2024· article· en· W4400429171 on OpenAlexvenueno aff
Wei Wang

Bibliographic record

VenueMaize Genomics and Genetics · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsnot available
Fundersnot available
KeywordsBiologyPhylogenetic treeVariation (astronomy)GenusEvolutionary biologyZea maysBotanyGeneticsGeneAgronomyPhysics

Abstract

fetched live from OpenAlex

This research explores the role of isoenzymatic variation in delineating phylogenetic relationships within the genus Zea , which includes maize ( Zea mays ) and its wild relatives, the teosintes. The primary aim is to synthesize existing research on isoenzymatic markers to understand their effectiveness in resolving phylogenetic relationships and uncovering genetic diversity within Zea . The methodology involved a comprehensive search of peer-reviewed studies focusing on isoenzymatic variation, phylogenetic analysis, and genetic diversity in Zea , utilizing databases such as PubMed, Web of Science, and Scopus. Studies were selected based on their relevance, methodological rigor, and contributions to the field. Key findings indicate that isoenzymatic markers are effective in identifying genetic differentiation between maize and teosinte species, supporting the hypothesis of a single domestication event from Zea mays ssp. parviglumis . Isoenzymatic data also reveal significant genetic diversity within teosinte populations and highlight the role of hybridization and introgression in shaping the genetic landscape of modern maize. When integrated with molecular markers like SSRs, SNPs, and cpDNA, isoenzymatic data provide a more comprehensive understanding of phylogenetic relationships and evolutionary processes within Zea . The research underscores the strengths and limitations of isoenzymatic markers, emphasizing their value in functional genetic studies despite their lower resolution compared to DNA-based markers. Recommendations for future research include expanding the geographic and taxonomic scope of isoenzymatic studies, employing advanced analytical techniques, and integrating isoenzymatic data with genomic and proteomic analyses to enhance phylogenetic resolution.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.939
Threshold uncertainty score0.217

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 head0.025
GPT teacher head0.195
Teacher spread0.170 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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