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Mendeley readers
Attention Score in Context
| Title |
Accuracy of genomic prediction for growth and carcass traits in Chinese triple-yellow chickens
|
|---|---|
| Published in |
BMC Genomic Data, October 2014
|
| DOI | 10.1186/s12863-014-0110-y |
| Pubmed ID | |
| Authors |
Tianfei Liu, Hao Qu, Chenglong Luo, Dingming Shu, Jie Wang, Mogens Sandø Lund, Guosheng Su |
| Abstract |
Growth and carcass traits are very important traits for broiler chickens. However, carcass traits can only be measured postmortem. Genomic selection may be a powerful tool for such traits because of its accurate prediction of breeding values of animals without own phenotypic information. This study investigated the efficiency of genomic prediction in Chinese triple-yellow chickens. As a new line, Chinese triple-yellow chicken was developed by cross-breeding and had a small effective population. Two growth traits and three carcass traits were analyzed: body weight at 6 weeks, body weight at 12 weeks, eviscerating percentage, breast muscle percentage and leg muscle percentage. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 32 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 6 | 19% |
| Researcher | 5 | 16% |
| Student > Ph. D. Student | 5 | 16% |
| Student > Doctoral Student | 4 | 13% |
| Other | 4 | 13% |
| Other | 6 | 19% |
| Unknown | 2 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 34% |
| Biochemistry, Genetics and Molecular Biology | 5 | 16% |
| Medicine and Dentistry | 3 | 9% |
| Environmental Science | 2 | 6% |
| Nursing and Health Professions | 2 | 6% |
| Other | 4 | 13% |
| Unknown | 5 | 16% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 16 October 2014.
All research outputs
#22,759,452
of 25,374,647 outputs
Outputs from BMC Genomic Data
#1,008
of 1,204 outputs
Outputs of similar age
#228,929
of 268,076 outputs
Outputs of similar age from BMC Genomic Data
#22
of 28 outputs
Altmetric has tracked 25,374,647 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,204 research outputs from this source. They receive a mean Attention Score of 4.3. This one is in the 1st percentile – i.e., 1% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 268,076 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.