More than meets the AI: The possibilities and limits of machine learning in olfaction.

Can machine learning crack the code in the nose? Over the past decade, studies tried to solve the relation between chemical structure and sensory quality with Big Data. These studies advanced computational models of the olfactory stimulus, utilizing artificial intelligence to mine for clear correlations between chemistry and psychophysics. Computational perspectives promised to solve the mystery of olfaction with more data and better data processing tools.

Climate change attribution and legal contexts: evidence and the role of storylines.

In a recent very influential court case, , climate scientist Kevin Trenberth used the "storyline" approach to extreme event attribution to argue that greenhouse warming had affected and will affect extreme events in their regions to such an extent that the plaintiffs already had been or will be harmed. The storyline approach to attribution is deterministic rather than probabilistic, taking certain factors as contingent and assessing the role of climate change conditional on those factors. The US Government's opposing expert witness argued that Trenberth had failed to make his case because "all his conclusions of the injuries to Plaintiffs suffer from the same failure to connect his conditional approach to Plaintiffs' local circumstances.

Climate scientists set the bar of proof too high.

Unlabelled: Standards of proof for attributing real world events/damage to global warming should be the same as in clinical or environmental lawsuits, argue Lloyd et al. The central question that we raise is effective communication. How can climate scientists best and effectively communicate their findings to crucial non-expert audiences, including public policy makers and civil society? To address this question, we look at the mismatch between what courts require and what climate scientists are setting as a bar of proof.

Environmental catastrophes, climate change, and attribution.

In our discussion of environmental and ecological catastrophes or disasters resulting from extreme weather events, we unite disparate literatures, the biological and the physical. Our goal is to tie together biological understandings of extreme environmental events with physical understandings of extreme weather events into joint causal accounts. This requires fine-grained descriptions, in both space and time, of the ecological, evolutionary, and biological moving parts of a system together with fine-grained descriptions, also in both space and time, of the extreme weather events.

Getting the Hologenome Concept Right: an Eco-Evolutionary Framework for Hosts and Their Microbiomes.

Given the complexity of host-microbiota symbioses, scientists and philosophers are asking questions at new biological levels of hierarchical organization-what is a holobiont and hologenome? When should this vocabulary be applied? Are these concepts a null hypothesis for host-microbe systems or limited to a certain spectrum of symbiotic interactions such as host-microbial coevolution? Critical discourse is necessary in this nascent area, but productive discourse requires that skeptics and proponents use the same lexicon. For instance, critiquing the hologenome concept is not synonymous with critiquing coevolution, and arguing that an entity is not a primary unit of selection dismisses the fact that the hologenome concept has always embraced multilevel selection. Holobionts and hologenomes are incontrovertible, multipartite entities that result from ecological, evolutionary, and genetic processes at various levels.

Model robustness as a confirmatory virtue: The case of climate science.

I propose a distinct type of robustness, which I suggest can support a confirmatory role in scientific reasoning, contrary to the usual philosophical claims. In model robustness, repeated production of the empirically successful model prediction or retrodiction against a background of independently-supported and varying model constructions, within a group of models containing a shared causal factor, may suggest how confident we can be in the causal factor and predictions/retrodictions, especially once supported by a variety of evidence framework. I present climate models of greenhouse gas global warming of the 20th Century as an example, and emphasize climate scientists' discussions of robust models and causal aspects.