東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Predictive Analytics and Data Manage...

Abell, Kaitlynn M.

FindBook

Google Book

Amazon

博客來

Predictive Analytics and Data Management in Beef Cattle Production Medicine.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Predictive Analytics and Data Management in Beef Cattle Production Medicine./
作者:	Abell, Kaitlynn M.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2017,
面頁冊數:	132 p.
附註:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Contained By:	Dissertation Abstracts International78-11B(E).
標題:	Epidemiology. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10270946
ISBN:	9780355050783

Predictive Analytics and Data Management in Beef Cattle Production Medicine.
Abell, Kaitlynn M.

Predictive Analytics and Data Management in Beef Cattle Production Medicine. - Ann Arbor : ProQuest Dissertations & Theses, 2017 - 132 p.

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)--Kansas State University, 2017.

Utilization of data analytics allows for rapid and real-time decision making in the food animal production industry. The objective of my research was to implement and utilize different data analytic strategies in multiple sectors of the beef cattle industry in order to determine management, health, and performance strategies.

ISBN: 9780355050783Subjects--Topical Terms:

568544
Epidemiology.

Predictive Analytics and Data Management in Beef Cattle Production Medicine.
LDR:02407nmm a2200325 4500 001 2154772
005 20180419121556.5
008 190424s2017 ||||||||||||||||| ||eng d
020 $a 9780355050783
035 $a (MiAaPQ)AAI10270946
035 $a (MiAaPQ)ksu:11570
035 $a AAI10270946
040 $a MiAaPQ $c MiAaPQ
100 1 $a Abell, Kaitlynn M. $3 3342501
245 1 0 $a Predictive Analytics and Data Management in Beef Cattle Production Medicine.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2017
300 $a 132 p.
500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Advisers: Brad J. White; Robert L. Larson.
502 $a Thesis (Ph.D.)--Kansas State University, 2017.
520 $a Utilization of data analytics allows for rapid and real-time decision making in the food animal production industry. The objective of my research was to implement and utilize different data analytic strategies in multiple sectors of the beef cattle industry in order to determine management, health, and performance strategies.
520 $a A retrospective analysis using reproductive and genomic records demonstrated that a bull will sire a larger number of calves in a multiple sire-pasture compared to other bulls in the same pasture. A further study was performed to determine if behavior differences existed among bulls in a multiple-sire pasture, and the ability of accelerometers to predict breeding behaviors. Machine learning techniques used classifiers on accelerometer data to predict behavior events lying, standing, walking, and mounting. The classifiers were able to accurately predict lying and standing, but walking and mounting resulted in a lower predictable accuracy due to the extremely low prevalence of these behaviors.
520 $a Finally, a new form of meta-analysis to the veterinary literature, a mixed treatment comparison, was able to accurately identify differences in metaphylactic antimicrobials on outcomes of bovine respiratory disease morbidity, mortality, and retreatment morbidity. The meta-analysis was not successful in determining the effects of metaphylactic antimicrobials on performance outcomes.
590 $a School code: 0100.
650 4 $a Epidemiology. $3 568544
650 4 $a Veterinary science. $3 3172798
690 $a 0766
690 $a 0778
710 2 $a Kansas State University. $b Diagnostic Medicine and Pathobiology. $3 3342502
773 0 $t Dissertation Abstracts International $g 78-11B(E).
790 $a 0100
791 $a Ph.D.
792 $a 2017
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10270946