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Record W4390474895 · doi:10.5376/ijmeb.2024.14.0001

Analysis of Adaptive Variation in Mammalian Genomes in Terrestrial and Water Ecosystems

2024· article· en· W4390474895 on OpenAlexvenueno aff
William M. Cook

Bibliographic record

VenueInternational Journal of Molecular Evolution and Biodiversity · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Phylogenetic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsVariation (astronomy)NicheEcologyBiologyEcosystemContext (archaeology)Adaptation (eye)Terrestrial ecosystemEvolutionary biology

Abstract

fetched live from OpenAlex

The study aims to analyze the characteristics and importance of adaptive variation in mammalian genomes in terrestrial and water ecosystems. To explore the basic concepts and measures of genome adaptive variation through insights into the context of the ecosystem and the role of mammals in it. The study covered the adaptive variation of mammals in terrestrial and water ecosystems, and examined the ecological factors affecting adaptive variation, such as niche, climate change and population genetic structure, and also explored the impact of environmental variation on adaptive variation and the importance of protecting mammalian diversity. The study provided valuable insights for deepening the understanding of adaptive variation in mammalian genomes and provided scientific basis for future research and conservation work in ecology 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 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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score0.177

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 head0.009
GPT teacher head0.220
Teacher spread0.210 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

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