Evidence map›Paper›PMID 39298007›Full record

ArticleTropical animal health and production2024

Machine learning-based early prediction of growth and morphological traits at yearling age in pure and hybrid goat offspring.

Hakan Erduran, Necati Esener, İsmail Keskin, Birol Dağ

Abstract read
PubMed Publisher
In one paragraph

Article in Tropical animal health and production, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
–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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. 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

4 authors.

Hakan ErduranBahri Dagdas International Agricultural Research Institute, Konya, Türkiye.
Necati EsenerBahri Dagdas International Agricultural Research Institute, Konya, Türkiye. necati.esener@tarimorman.gov.tr.ORCID http://orcid.org/0000-0002-2773-3234
İsmail KeskinFaculty of Agriculture, Department of Animal Science, Selcuk University, Konya, Türkiye.
Birol DağFaculty of Agriculture, Department of Animal Science, Selcuk University, Konya, Türkiye.

Funding

Tarimsal Araştirmalar ve Politikalar Genel Müdürlüğü, Türkiye Cumhuriyeti Tarim Ve Orman Bakanliği Tarimsal Araştirmalar ve Politikalar Genel Müdürlüğü, Türkiye Cumhuriyeti Tarim Ve Orman Bakanliği
6 · The paper itself

Abstract

The purpose of this study was to evaluate the performance of various prediction models in estimating the growth and morphological traits of pure Hair, Alpine × Hair F

Indexed as

GoatsMachine LearningAnimalsBreedingFemaleMaleNeural Networks, ComputerExtensive pasture systemsGoat breedingMachine learning in livestockPredictive modelling

Identifiers

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.