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Record W4415721783 · doi:10.5376/ija.2025.15.0025

Evolutionary Pathways of Fishes: Insights from Fossil Records and Molecular Phylogenetics

2025· article· W4415721783 on OpenAlexvenueno aff
Xianming Li, Lingfei Jin

Bibliographic record

VenueInternational Journal of Aquaculture · 2025
Typearticle
Language
FieldEarth and Planetary Sciences
TopicPaleontology and Evolutionary Biology
Canadian institutionsnot available
Fundersnot available
KeywordsMolecular clockPhylogenetic treeMolecular phylogeneticsFish <Actinopterygii>PhylogeneticsMolecular evolutionFossil Record

Abstract

fetched live from OpenAlex

Fish, as the oldest and diverse group of vertebrates, play an important role in the history of biological evolution. This study summarizes the main stages and characteristics of fish evolution pathways, focusing on combining fossil record with molecular phylogenetic research to reveal the evolution of fish from early jawless fish to modern diversified taxa. We reviewed the important discoveries of early jawless fish fossils and the outbreak of fish diversity during the Silurian and Devonian periods; explored the profound impact of the origin of the jaw on predation strategies, as well as the early differentiation of boneless and cartilage fish in the Paleozoic era. Analysis of morphological characteristics and molecular phylogenetic evidence of radial fin fish. Based on the latest molecular phylogenetic research, we reconstructed the evolutionary relationships of important fish populations and conducted case analysis in combination with key evolutionary events. Finally, we emphasize the importance of combining fossil evidence with molecular clock calibration, and combining fossil and molecular insights can help deepen our understanding of the origins and adaptive evolution mechanisms of fish biodiversity.

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)0.002
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0060.005
Science and technology studies0.0010.002
Scholarly communication0.0010.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.226
Teacher spread0.217 · 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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