Evidence map›Paper›PMID 41514788›Full record

ReviewAnimals : an open access journal from MDPI2025

Artificial Neural Networks for Predicting Emissions from the Livestock Sector: A Review.

Luciano Manuel Santoro, Provvidenza Rita D'Urso, Claudia Arcidiacono, Giovanni Cascone, Salvatore Coco

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Animals : an open access journal from MDPI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

5 authors.

Luciano Manuel SantoroBuilding and Land Engineering Section, Department of Agriculture, Food and Environment (Di3A), University of Catania, via S. Sofia 100, 95123 Catania, Italy.ORCID 0009-0004-6349-835X
Provvidenza Rita D'UrsoBuilding and Land Engineering Section, Department of Agriculture, Food and Environment (Di3A), University of Catania, via S. Sofia 100, 95123 Catania, Italy.ORCID 0000-0002-7058-1838
Claudia ArcidiaconoBuilding and Land Engineering Section, Department of Agriculture, Food and Environment (Di3A), University of Catania, via S. Sofia 100, 95123 Catania, Italy.ORCID 0000-0002-4639-6229
Giovanni CasconeBuilding and Land Engineering Section, Department of Agriculture, Food and Environment (Di3A), University of Catania, via S. Sofia 100, 95123 Catania, Italy.
Salvatore CocoDepartment of Electrical, Electronics and Informatics Engineering (DIEEI)-Section of Electrical, Electronics and Systems Engineering, University of Catania, via Santa Sofia n° 64, 95123 Catania, Italy.

Funding

European Union (NextGeneration EU), through the MUR-PNRR project SAMOTHRACE CUP: E63C22000900006; CODE_ECS00000022
6 · The paper itself

Abstract

Gaseous emissions from livestock facilities pose environmental and health concerns. Monitoring pollutant gases is essential to mitigate impact and enhance the sustainability of livestock systems. Emerging Artificial Intelligence (AI) technologies-particularly Artificial Neural Networks (ANNs)-offer advanced tools to address these challenges by improving livestock monitoring and management. Following PRISMA guidelines, 18 studies published between 2007 and 2024 were selected from Web of Science

Indexed as

artificial neural networkemissionhousing systemlivestock productionmachine learningmanure management

Identifiers

PMID41514788
PMCPMC12784785

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.