ReviewJournal of animal science2026
ASAS-NANP symposium: mathematical modeling in animal nutrition: review of time series techniques and machine learning models for advancing livestock production.
Review in Journal of animal science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Correlated data, including time series where observations depend on previous values, frequently occur in various livestock production studies. There are many statistical models and machine learning algorithms that have been developed to analyze this type of data. This paper provides a review of these models. Specifically, a detailed overview of the AutoRegressive Integrated Moving Average with eXogenous inputs (ARIMAX) model, Dynamic Models and Kalman Filtering (KF), Deep Neural Network (DNN), Recurrent Neural Network (RNN), and Transformer models are provided. This review focuses on steps to train different statistical and machine learning models and lists the advantages and limitations of each model.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.