Informing UK governance of resilience to climate risks: improving the local evidence-base

International assessments of evidence on climate change (e.g. Intergovernmental Panel on Climate Change, IPCC) or national climate change risk assessments (e.g. UK Climate Change Risk Assessment, CCRA) do not offer a sufficiently granular perspective on climate impacts to adequately inform governance of resilience to climate risks at the local level. Using an analysis of UK decision-makers managing and responding to heatwaves and flood risks, this paper argues how more robust local evidence is needed to inform decision-making regarding adaptation options for enhancing local resilience. We identify evidence gaps and issues relating to local climate change impacts, including sources and quality of evidence used, adequacy and accessibility of evidence available, ill-communicated evidence and conflicting or misused evidence. A lack of appreciation regarding how scientific evidence and personal judgement can mutually enhance the quality of decision-making underpins all of these gaps. Additionally, we find that the majority of evidence currently used is reductively based upon socio-economic and physical characteristics of climate risks. We argue that a step change is needed in local climate resilience that moves beyond current physical and socio-economic risk characterisation to a more inclusive co-constitution of social and politically defined climate risks at the local scale that are better aligned with the local impacts felt and needs of stakeholders

Maternal depressive symptoms, and not anxiety symptoms, are associated with positive mother–child reporting discrepancies of internalizing problems in children: a report on the TRAILS Study

Maternal internalizing problems affect reporting of child’s problem behavior. This study addresses the relative effects of maternal depressive symptoms versus anxiety symptoms and the association with differential reporting of mother and child on child’s internalizing problems. The study sample comprised a cohort of 1,986 10- to 12-year-old children and their mothers from the Dutch general population in a cross sectional setup. Children’s internalizing problems were assessed with the DSM-IV anxiety and affective problem scales of the Child Behavior Checklist (CBCL) and the Youth Self-Report (YSR). Current maternal internalizing problems were assessed with the depressive and anxiety symptom scales of the Depression Anxiety Stress Scale (DASS), while the TRAILS Family History Interview (FHI) measured lifetime maternal depression and anxiety. Results show that current and lifetime maternal depressive symptoms were associated with positive mother–child reporting discrepancies (i.e. mothers reporting more problems than their child). Considering the small amount of variance explained, we conclude that maternal depressive symptoms do not bias maternal reporting on child’s internalizing problems to a serious degree. Studies concerning long term consequences of mother–child reporting discrepancies on child’s internalizing problems are few, but show a risk for adverse outcome. More prognostic research is needed

Ultramafic vegetation and soils in the circumboreal region of the Northern Hemisphere

The paper summarizes literature on climate, soil chemistry, vegetation and metal accumulation by plants found on ultramafic substrata in the circumboreal zone (sensu Takhtajan, Floristic regions of the world, 1986) of the Northern Hemisphere. We present a list of 50 endemic species and 18 ecotypes obligate to ultramafic soils from the circumboreal region of Holarctic, as well as 30 and 2 species of Ni and Zn hyperaccumulators, respectively. The number of both endemics and hyperaccumulators are markedly lower compared to that of the Mediterranean and tropical regions. The diversity of plant communities on ultramafics soils of the circumboral region is also described. The underlying causes for the differences of ultramafic flora between arctic, cold, cool temperate and Mediterranean and tropical regions are also discussed. © 2018, The Ecological Society of Japan

Neutrino interaction classification with a convolutional neural network in the DUNE far detector

The Deep Underground Neutrino Experiment is a next-generation neutrino oscillation experiment that aims to measure CP-violation in the neutrino sector as part of a wider physics program. A deep learning approach based on a convolutional neural network has been developed to provide highly efficient and pure selections of electron neutrino and muon neutrino charged-current interactions. The electron neutrino (antineutrino) selection efficiency peaks at 90% (94%) and exceeds 85% (90%) for reconstructed neutrino energies between 2–5 GeV. The muon neutrino (antineutrino) event selection is found to have a maximum efficiency of 96% (97%) and exceeds 90% (95%) efficiency for reconstructed neutrino energies above 2 GeV. When considering all electron neutrino and antineutrino interactions as signal, a selection purity of 90% is achieved. These event selections are critical to maximize the sensitivity of the experiment to CP-violating effects

Design, construction and operation of the ProtoDUNE-SP Liquid Argon TPC

The ProtoDUNE-SP detector is a single-phase liquid argon time projection chamber (LArTPC) that was constructed and operated in the CERN North Area at the end of the H4 beamline. This detector is a prototype for the first far detector module of the Deep Underground Neutrino Experiment (DUNE), which will be constructed at the Sandford Underground Research Facility (SURF) in Lead, South Dakota, U.S.A. The ProtoDUNE-SP detector incorporates full-size components as designed for DUNE and has an active volume of 7 × 6 × 7.2 m3. The H4 beam delivers incident particles with well-measured momenta and high-purity particle identification. ProtoDUNE-SP's successful operation between 2018 and 2020 demonstrates the effectiveness of the single-phase far detector design. This paper describes the design, construction, assembly and operation of the detector components