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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Causality guided machine learning model on wetland CH4 emissions across global wetlands
|
|---|---|
| Published in |
Agricultural & Forest Meteorology, September 2022
|
| DOI | 10.1016/j.agrformet.2022.109115 |
| Authors |
Kunxiaojia Yuan, Qing Zhu, Fa Li, William J. Riley, Margaret Torn, Housen Chu, Gavin McNicol, Min Chen, Sara Knox, Kyle Delwiche, Huayi Wu, Dennis Baldocchi, Hongxu Ma, Ankur R. Desai, Jiquan Chen, Torsten Sachs, Masahito Ueyama, Oliver Sonnentag, Manuel Helbig, Eeva-Stiina Tuittila, Gerald Jurasinski, Franziska Koebsch, David Campbell, Hans Peter Schmid, Annalea Lohila, Mathias Goeckede, Mats B. Nilsson, Thomas Friborg, Joachim Jansen, Donatella Zona, Eugenie Euskirchen, Eric J. Ward, Gil Bohrer, Zhenong Jin, Licheng Liu, Hiroki Iwata, Jordan Goodrich, Robert Jackson |
X Demographics
The data shown below were collected from the profiles of 18 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 7 | 39% |
| United Kingdom | 1 | 6% |
| Netherlands | 1 | 6% |
| France | 1 | 6% |
| Unknown | 8 | 44% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 50% |
| Scientists | 9 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 48 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 48 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 10 | 21% |
| Student > Ph. D. Student | 8 | 17% |
| Student > Master | 4 | 8% |
| Other | 2 | 4% |
| Student > Doctoral Student | 1 | 2% |
| Other | 5 | 10% |
| Unknown | 18 | 38% |
| Readers by discipline | Count | As % |
|---|---|---|
| Environmental Science | 11 | 23% |
| Earth and Planetary Sciences | 5 | 10% |
| Agricultural and Biological Sciences | 2 | 4% |
| Physics and Astronomy | 2 | 4% |
| Computer Science | 1 | 2% |
| Other | 3 | 6% |
| Unknown | 24 | 50% |
Attention Score in Context
This research output has an Altmetric Attention Score of 21. 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 27 October 2022.
All research outputs
#1,786,971
of 25,837,817 outputs
Outputs from Agricultural & Forest Meteorology
#114
of 2,463 outputs
Outputs of similar age
#38,154
of 431,624 outputs
Outputs of similar age from Agricultural & Forest Meteorology
#8
of 63 outputs
Altmetric has tracked 25,837,817 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 92nd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,463 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.8. This one has done well, scoring higher than 89% of its peers.
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 431,624 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 63 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.