Quantitative reconstructions in palaeolimnology: new paradigm or sick science?
By S. Juggins. 2013
379 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Atmospheric Science, Ecological Modeling
View Paper: https://www.semanticscholar.org/paper/70a503a4d6f12a885dd25a7f57f249019a835477
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- J. R. Allen
- M. Salter-Townshend
- Brian Huntley
- James Sweeney
- T. K. Doan
- Andrew C. Parnell
- John Haslett
In Atmospheric Science
- J. R. Allen
- M. Salter-Townshend
- Brian Huntley
- James Sweeney
- T. K. Doan
- Andrew C. Parnell
- John Haslett
In Ecological Modeling
- J. R. Allen
- M. Salter-Townshend
- Brian Huntley
- James Sweeney
- T. K. Doan
- Andrew C. Parnell
- John Haslett
Citations in the corpus
- Bayesian inference for palaeoclimate with time uncertainty and stochastic volatility (2015, methodology): “We infer these three variables via a jointly defined likelihood, as reconstructing variables individually can lead to further difficulties (Juggins, 2013).”
- Looking forward through the past: identification of 50 priority research questions in palaeoecology (2014, neutral): “The fi rst is to disentangle the effects of multiple causal factors on palaeoecological records at single sites and across multisite networks (Cunning-ham et al. 2013; Juggins 2013).”