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Near-real time forecasting and change detection for an open ecosystem

Overview of attention for research output published on figshare, July 2020
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (59th percentile)
  • Good Attention Score compared to outputs of the same age and source (75th percentile)

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Title
Near-real time forecasting and change detection for an open ecosystem
Published on
figshare, July 2020
DOI 10.6084/m9.figshare.12612941
Authors

Slingsby, Jasper, Wilson, Adam, Moncrieff, Glenn

Abstract

Presentation given for the GEO BON Open Science Meeting, July 2020 https://conf2020.geobon.org/ - complete with narration!We present a hierarchical Bayesian modelling framework that allows us to forecast remotely sensed vegetation indices in a fire-dependent and seasonally fluctuating ecosystem, the Fynbos of South Africa. This framework allows several applications including: 1) detecting near real-time changes in the state of the ecosystem by comparing observed vegetation signal with the model forecasts; 2) determining the influence of plant traits on vegetation productivity and seasonality; 3) forecasting changes in vegetation productivity and seasonality under altered climate or community composition; and 4) estimating ecosystem properties like leaf area index (LAI) or above ground biomass. As such, it provides the means to draw linkages across and/or monitor several EBV classes.

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Attention Score in Context

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 07 July 2020.
All research outputs
#8,239,481
of 24,953,268 outputs
Outputs from figshare
#7,741
of 25,051 outputs
Outputs of similar age
#163,402
of 403,323 outputs
Outputs of similar age from figshare
#172
of 705 outputs
Altmetric has tracked 24,953,268 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 25,051 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.0. This one has gotten more attention than average, scoring higher than 68% 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 403,323 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 59% of its contemporaries.
We're also able to compare this research output to 705 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 75% of its contemporaries.