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Hierarchical object-based stochastic modeling of fluvial reservoirs

Overview of attention for article published in Mathematical Geology, October 1996
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (66th percentile)

Mentioned by

patent
5 patents

Citations

dimensions_citation
141 Dimensions

Readers on

mendeley
63 Mendeley
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Title
Hierarchical object-based stochastic modeling of fluvial reservoirs
Published in
Mathematical Geology, October 1996
DOI 10.1007/bf02066005
Authors

Clayton V. Deutsch, Libing Wang

Mendeley readers

The data shown below were compiled from readership statistics for 63 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Spain 1 2%
Portugal 1 2%
Nigeria 1 2%
Unknown 58 92%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 32%
Researcher 16 25%
Student > Master 8 13%
Professor > Associate Professor 6 10%
Other 4 6%
Other 7 11%
Unknown 2 3%
Readers by discipline Count As %
Earth and Planetary Sciences 26 41%
Engineering 16 25%
Environmental Science 4 6%
Mathematics 2 3%
Business, Management and Accounting 1 2%
Other 2 3%
Unknown 12 19%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 28 May 2013.
All research outputs
#2,818,252
of 10,492,544 outputs
Outputs from Mathematical Geology
#6
of 59 outputs
Outputs of similar age
#32,318
of 99,389 outputs
Outputs of similar age from Mathematical Geology
#1
of 1 outputs
Altmetric has tracked 10,492,544 research outputs across all sources so far. This one has received more attention than most of these and is in the 53rd percentile.
So far Altmetric has tracked 59 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 13th percentile – i.e., 13% 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 99,389 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 66% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them