Evidence map›Paper›PMID 36146158›Full record

ReviewSensors (Basel, Switzerland)2022

Dairy Cattle Rumen Bolus Developments with Special Regard to the Applicable Artificial Intelligence (AI) Methods.

Éva Hajnal, Levente Kovács, Gergely Vakulya

Abstract readReview
In one paragraph

Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

  1. Heat Stress Detection in Dairy Cattle Using Intraruminal Bolus Sensors.Animals : an open access journal from MDPI · 2026
    Review
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Review
  9. Review
  10. Article
  11. Review
  12. Review
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

3 authors.

Éva HajnalAlba Regia Technical Faculty, Óbuda University, 1034 Budapest, Hungary.ORCID 0000-0002-9498-5052
Levente KovácsInstitute of Animal Sciences, Hungarian University of Agricultural and Life Sciences, 2100 Gödöllő, Hungary.
Gergely VakulyaAlba Regia Technical Faculty, Óbuda University, 1034 Budapest, Hungary.ORCID 0000-0002-4289-5469

Funding

National Research, Development and Innovation Office 2020-1.1.2-PIACI-KFI-2020-00109National Research, Development and Innovation Office K-134204NKFI Hungary GINOP_PLUSZ-2.1.1-21-2022-00164
6 · The paper itself

Abstract

It is a well-known worldwide trend to increase the number of animals on dairy farms and to reduce human labor costs. At the same time, there is a growing need to ensure economical animal husbandry and animal welfare. One way to resolve the two conflicting demands is to continuously monitor the animals. In this article, rumen bolus sensor techniques are reviewed, as they can provide lifelong monitoring due to their implementation. The applied sensory modalities are reviewed also using data transmission and data-processing techniques. During the processing of the literature, we have given priority to artificial intelligence methods, the application of which can represent a significant development in this field. Recommendations are also given regarding the applicable hardware and data analysis technologies. Data processing is executed on at least four levels from measurement to integrated analysis. We concluded that significant results can be achieved in this field only if the modern tools of computer science and intelligent data analysis are used at all levels.

Indexed as

Cattle DiseasesRumenAnimal HusbandryAnimalsAnimal WelfareArtificial IntelligenceCattleHumansartificial intelligencedairy cattledata processingmachine learningrumen bolus

Identifiers

PMID36146158
PMCPMC9505622

What OpenQuestion holds

Textmetadata
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.