Evidence map›Paper›PMID 37570221›Full record

ArticleAnimals : an open access journal from MDPI2023

Field Implementation of Forecasting Models for Predicting Nursery Mortality in a Midwestern US Swine Production System.

Edison S Magalhaes, Danyang Zhang, Chong Wang, Pete Thomas, Cesar A A Moura, Derald J Holtkamp, Giovani Trevisan, Christopher Rademacher, Gustavo S Silva, Daniel C L Linhares

Abstract read
In one paragraph

Article in Animals : an open access journal from MDPI, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Edison S MagalhaesDepartment of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA 50011, USA.
Danyang ZhangDepartment of Statistics, College of Liberal Arts and Sciences, Iowa State University, Ames, IA 50011, USA.
Chong WangDepartment of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA 50011, USA.ORCID 0000-0003-4489-4344
Pete ThomasIowa Select Farms, Iowa Falls, IA 50126, USA.
Cesar A A MouraIowa Select Farms, Iowa Falls, IA 50126, USA.
Derald J HoltkampDepartment of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA 50011, USA.
Giovani TrevisanDepartment of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA 50011, USA.ORCID 0000-0002-4980-526X
Christopher RademacherDepartment of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA 50011, USA.
Gustavo S SilvaDepartment of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA 50011, USA.ORCID 0000-0001-5884-8803
Daniel C L LinharesDepartment of Veterinary Diagnostic and Production Animal Medicine, College of Veterinary Medicine, Iowa State University, Ames, IA 50011, USA.ORCID 0000-0001-7788-9942

Funding

Iowa State University C. R. Henderson Fund for Excellence in Predictive Inference and Its ApplicationsNational Institute of Food and Agriculture #022-68014-36668
6 · The paper itself

Abstract

The performance of five forecasting models was investigated for predicting nursery mortality using the master table built for 3242 groups of pigs (~13 million animals) and 42 variables, which concerned the pre-weaning phase of production and conditions at placement in growing sites. After training and testing each model's performance through cross-validation, the model with the best overall prediction results was the Support Vector Machine model in terms of Root Mean Squared Error (RMSE = 0.406), Mean Absolute Error (MAE = 0.284), and Coefficient of Determination (R

Indexed as

data-wranglingforecastingmachine-learningmortalityswine

Identifiers

PMID37570221
PMCPMC10417698

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.