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Suitability of different machine learning algorithms for the classification of the proportion of grassland-based forages at the herd level using mid-infrared spectral information from routine milk control.

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Title: Suitability of different machine learning algorithms for the classification of the proportion of grassland-based forages at the herd level using mid-infrared spectral information from routine milk control.
Authors: Birkinshaw, A.1 (AUTHOR), Sutter, M.1 (AUTHOR), Nussbaum, M.1,2 (AUTHOR), Kreuzer, M.3 (AUTHOR), Reidy, B.1 (AUTHOR) beat.reidy@bfh.ch
Source: Journal of Dairy Science. Dec2024, Vol. 107 Issue 12, p10724-10737. 14p.
Database: Business Source Ultimate
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  Availability: 0
Header DbId: bsu
DbLabel: Business Source Ultimate
An: 181160343
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Suitability of different machine learning algorithms for the classification of the proportion of grassland-based forages at the herd level using mid-infrared spectral information from routine milk control.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Dairy+Science%22">Journal of Dairy Science</searchLink>. Dec2024, Vol. 107 Issue 12, p10724-10737. 14p.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=bsu&AN=181160343
RecordInfo BibRecord:
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      – Type: doi
        Value: 10.3168/jds.2024-25090
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 14
        StartPage: 10724
    Titles:
      – TitleFull: Suitability of different machine learning algorithms for the classification of the proportion of grassland-based forages at the herd level using mid-infrared spectral information from routine milk control.
        Type: main
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          Name:
            NameFull: Birkinshaw, A.
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            NameFull: Sutter, M.
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            NameFull: Nussbaum, M.
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            NameFull: Kreuzer, M.
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            NameFull: Reidy, B.
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            – D: 01
              M: 12
              Text: Dec2024
              Type: published
              Y: 2024
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              Value: 00220302
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              Value: 107
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              Value: 12
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