bumper v1.0: a bayesian user-friendly model for palaeo-environmental reconstruction
Clicks: 6
ID: 219983
2017
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Emerging Content
1.5
/100
6 views
5 readers
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #128 of 133 articles by views in international journal of quantum chemistry
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 133 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
We describe the Bayesian user-friendly model for
palaeo-environmental reconstruction (BUMPER), a Bayesian transfer function
for inferring past climate and other environmental variables from
microfossil assemblages. BUMPER is fully self-calibrating, straightforward
to apply, and computationally fast, requiring ∼ 2 s to
build a 100-taxon model from a 100-site training set on a standard personal
computer. We apply the model's probabilistic framework to generate thousands
of artificial training sets under ideal assumptions. We then use these to
demonstrate the sensitivity of reconstructions to the characteristics of the
training set, considering assemblage richness, taxon tolerances, and the
number of training sites. We find that a useful guideline for the size of a
training set is to provide, on average, at least 10 samples of each taxon.
We demonstrate general applicability to real data, considering three
different organism types (chironomids, diatoms, pollen) and different
reconstructed variables. An identically configured model is used in each
application, the only change being the input files that provide the
training-set environment and taxon-count data. The performance of BUMPER is
shown to be comparable with weighted average partial least squares (WAPLS)
in each case. Additional artificial datasets are constructed with similar
characteristics to the real data, and these are used to explore the reasons
for the differing performances of the different training sets.
| Reference Key |
holden2017geoscientificbumper
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;P. B. Holden;H. J. B. Birks;S. J. Brooks;M. B. Bush;G. M. Hwang;F. Matthews-Bird;B. G. Valencia;R. van Woesik |
| Journal | international journal of quantum chemistry |
| Year | 2017 |
| DOI |
10.5194/gmd-10-483-2017
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.