AIMC Topic: Life History Traits
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Explainable machine learning models of major crop traits from satellite-monitored continent-wide field trial data.
Nature plants
Oct 4, 2021
Four species of grass generate half of all human-consumed calories. However, abundant biological data on species that produce our food remain largely inaccessible, imposing direct barriers to understanding crop yield and fitness traits. Here, we asse...
Augmenting biologging with supervised machine learning to study behavior of the medusa .
The Journal of experimental biology
Aug 23, 2019
Zooplankton play critical roles in marine ecosystems, yet their fine-scale behavior remains poorly understood because of the difficulty in studying individuals Here, we combine biologging with supervised machine learning (ML) to propose a pipeline f...
Machine learning-enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions.
Plant physiology
Nov 3, 2021
Sorghum (Sorghum bicolor) is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is studied as a feedstock for biofuel and forage. Mechanistic modeling suggests that reducing stomatal conductance ...
Imitative and Direct Learning as Interacting Factors in Life History Evolution.
Artificial life
Jan 1, 2017
The idea that lifetime learning can have a significant effect on life history evolution has recently been explored using a series of artificial life simulations. These involved populations of competing individuals evolving by natural selection to lea...
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