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Key Points Optical, very high frequency, and low‐frequency observations are combined to analyze the transition from upward to horizontal propagation of initial in‐cloud lightning A drop in the optical blue‐to‐red ratio indicates when the dominant illumination process changes from streamers to likely stepped leader We find for in‐cloud lightning that the upward initial leader and the horizontal stepped leader could be physically different 

The organic carbon (Corg) stored in seagrass meadows is globally significant and could be relevant in strategies to mitigate increasing CO2 concentration in the atmosphere. Most of that stored Corg is in the soils that underlie the seagrasses. We explored how seagrass and soil characteristics vary among seagrass meadows across the geographic range of turtlegrass (Thalassia testudinum) with a goal of illuminating the processes controlling soil organic carbon (Corg) storage spanning 23° of latitude. Seagrass abundance (percent cover, biomass, and canopy height) varied by over an order of magnitude across sites, and we found high variability in soil characteristics, with Corg ranging from 0.08 to 12.59% dry weight. Seagrass abundance was a good predictor of the Corg stocks in surficial soils, and the relative importance of seagrass-derived soil Corg increased as abundance increased. These relationships suggest that first-order estimates of surficial soil Corg stocks can be made by measuring seagrass abundance and applying a linear transfer function. The relative availability of the nutrients N and P to support plant growth was also correlated with soil Corg stocks. Stocks were lower at N-limited sites than at P-limited ones, but the importance of seagrass-derived organic matter to soil Corg stocks was not a function of nutrient limitation status. This finding seemed at odds with our observation that labile standard substrates decomposed more slowly at N-limited than at P-limited sites, since even though decomposition rates were 55% lower at N-limited sites, less Corg was accumulating in the soils. The dependence of Corg stocks and decomposition rates on nutrient availability suggests that eutrophication is likely to exert a strong influence on carbon storage in seagrass meadows. 

Abstract Scattering of high energy particles from nucleons probes their structure, as was done in the experiments that established the non-zero size of the proton using electron beams 1 . The use of charged leptons as scattering probes enables measuring the distribution of electric charges, which is encoded in the vector form factors of the nucleon 2 . Scattering weakly interacting neutrinos gives the opportunity to measure both vector and axial vector form factors of the nucleon, providing an additional, complementary probe of their structure. The nucleon transition axial form factor, F A , can be measured from neutrino scattering from free nucleons, ν μ n  →  μ − p and $${\bar{\nu }}_{\mu }p\to {\mu }^{+}n$$ ν ¯ μ p → μ + n , as a function of the negative four-momentum transfer squared ( Q 2 ). Up to now, F A ( Q 2 ) has been extracted from the bound nucleons in neutrino–deuterium scattering 3–9 , which requires uncertain nuclear corrections 10 . Here we report the first high-statistics measurement, to our knowledge, of the $${\bar{\nu }}_{\mu }\,p\to {\mu }^{+}n$$ ν ¯ μ p → μ + n cross-section from the hydrogen atom, using the plastic scintillator target of the MINERvA 11 experiment, extracting F A from free proton targets and measuring the nucleon axial charge radius, r A , to be 0.73 ± 0.17 fm. The antineutrino–hydrogen scattering presented here can access the axial form factor without the need for nuclear theory corrections, and enables direct comparisons with the increasingly precise lattice quantum chromodynamics computations 12–15 . Finally, the tools developed for this analysis and the result presented are substantial advancements in our capabilities to understand the nucleon structure in the weak sector, and also help the current and future neutrino oscillation experiments 16–20 to better constrain neutrino interaction models. 

Abstract We compare different neural network architectures for machine learning algorithms designed to identify the neutrino interaction vertex position in the MINERvA detector. The architectures developed and optimized by hand are compared with the architectures developed in an automated way using the package “Multi-node Evolutionary Neural Networks for Deep Learning” (MENNDL), developed at Oak Ridge National Laboratory. While the domain-expert hand-tuned network was the best performer, the differences were negligible and the auto-generated networks performed as well. There is always a trade-off between human, and computer resources for network optimization and this work suggests that automated optimization, assuming resources are available, provides a compelling way to save significant expert time.