| Title |
Instantaneous and non-destructive relative water content estimation from deep learning applied to resonant ultrasonic spectra of plant leaves
|
|---|---|
| Published in |
Plant Methods, November 2019
|
| DOI | 10.1186/s13007-019-0511-z |
| Pubmed ID | |
| Authors |
María Dolores Fariñas, Daniel Jimenez-Carretero, Domingo Sancho-Knapik, José Javier Peguero-Pina, Eustaquio Gil-Pelegrín, Tomás Gómez Álvarez-Arenas |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 2 | 33% |
| United Kingdom | 1 | 17% |
| Unknown | 3 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 50% |
| Scientists | 2 | 33% |
| Science communicators (journalists, bloggers, editors) | 1 | 17% |
Mendeley readers
The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 67 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 21% |
| Student > Ph. D. Student | 10 | 15% |
| Student > Doctoral Student | 6 | 9% |
| Student > Master | 4 | 6% |
| Professor > Associate Professor | 2 | 3% |
| Other | 5 | 7% |
| Unknown | 26 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 9 | 13% |
| Agricultural and Biological Sciences | 9 | 13% |
| Computer Science | 7 | 10% |
| Biochemistry, Genetics and Molecular Biology | 2 | 3% |
| Environmental Science | 2 | 3% |
| Other | 11 | 16% |
| Unknown | 27 | 40% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 15 May 2020.
All research outputs
#8,083,101
of 24,417,958 outputs
Outputs from Plant Methods
#535
of 1,178 outputs
Outputs of similar age
#143,355
of 371,382 outputs
Outputs of similar age from Plant Methods
#17
of 45 outputs
Altmetric has tracked 24,417,958 research outputs across all sources so far. This one has received more attention than most of these and is in the 66th percentile.
So far Altmetric has tracked 1,178 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.5. This one has gotten more attention than average, scoring higher than 53% 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 371,382 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 61% of its contemporaries.
We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.