Average cosine similarity of interaction coefficients (<i>b</i><sub>ij</sub>) where under varying time intervals (Δt) and noise levels for different methods, using simulated datasets that reflect a stabilizing system of three species.

Average cosine similarity of interaction coefficients (<i>b</i><sub>ij</sub>) where under varying time intervals (Δt) and noise levels for different methods, using simulated datasets that reflect a stabilizing system of three species.

<p>Average cosine similarity of interaction coefficients (<i>b</i><sub>ij</sub>) where under varying time intervals (Δt) and noise levels for different methods, using simulated datasets that reflect a stabilizing system of three species.</p>

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Bibliographic Details
Main Author:	Yue Huang (285485) (author)
Other Authors:	Tianqi Tang (6535856) (author), Xiaowu Dai (10073580) (author), Fengzhu Sun (15206) (author)
Published:	2025
Subjects:	Ecology Inorganic Chemistry Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified varying noise levels recovering interaction coefficients predicting species trajectories leverages relative abundances investigate system dynamics interactions affect stability exploring population dynamics understanding microbial interactions infer microbial interactions absolute abundance data stylonychia pustula </ novel framework tailored div >< p solving generalized lotka compositional data using compositional data generalized lotka microbial communities using simulations caudatum </ robust framework iterative lotka generalized lv compositional lv compositional constraints world datasets widely used temporal resolutions significant potential parameter estimation often unavailable nonlinear refinements mixed culture microbiome studies iterative refinements iterative approach host health hare predator
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