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Peer critique is a social epistemic practice. Productive engagement in critique requires attention to its affective dynamics. We conducted discourse analysis of two focus groups across two rounds of peer critique. The groups showed different understandings and enactments of epistemic ideals for critique. The negative epistemic affect that emerged signaled differences in students’ epistemic ideals. Yet students strategically managed affect and negotiated epistemic ideals, thereby transforming affective tensions into opportunities for shared understanding. 

Abstract Semidiurnal variability of alongshore currents on the inner shelf of the Southern California Bight is investigated using a 7‐year velocity and pressure time series. Analysis reveals that the ‐frequency alongshore current varies significantly over spatial scales of O(10 km), inconsistent with the expected progressive surface tide. Instead, the observed variability is attributed to the influence of a northward‐propagating, superinertial baroclinic coastal trapped wave (CTW) that generates a quasi‐barotropic flow, defined as the portion of the depth‐averaged alongshore current that is not directly driven by the surface tide. A superinertial CTW model, forced by realistic bathymetry and stratification conditions, suggests that the dominant mode of variability is likely a mode‐1 CTW with a wavelength of approximately 40 km. The observations and model also reveal that seasonal changes in stratification modulate the wavelength and phase speed of the CTW, leading to a seasonal pattern in the phasing of the quasi‐barotropic alongshore flow. These findings provide a new perspective on the complex dynamics governing semidiurnal variability of alongshore currents on the inner shelf of the Southern California Bight and highlight the importance of considering the effects of superinertial CTWs when examining coastal dynamics. 

Abstract The drag coefficient, Stanton number and Dalton number are of particular importance for estimating the surface turbulent fluxes of momentum, heat and water vapor using bulk parameterization. Although these bulk transfer coefficients have been extensively studied over the past several decades in marine and large‐lake environments, there are no studies analyzing their variability for smaller lakes. Here, we evaluated these coefficients through directly measured surface fluxes using the eddy‐covariance technique over more than 30 lakes and reservoirs of different sizes and depths. Our analysis showed that the transfer coefficients (adjusted to neutral atmospheric stability) were generally within the range reported in previous studies for large lakes and oceans. All transfer coefficients exhibit a substantial increase at low wind speeds (<3 m s⁻¹), which was found to be associated with the presence of gusts and capillary waves (except Dalton number). Stanton number was found to be on average a factor of 1.3 higher than Dalton number, likely affecting the Bowen ratio method. At high wind speeds, the transfer coefficients remained relatively constant at values of 1.6·10⁻³, 1.4·10⁻³, 1.0·10⁻³, respectively. We found that the variability of the transfer coefficients among the lakes could be associated with lake surface area. In flux parameterizations at lake surfaces, it is recommended to consider variations in the drag coefficient and Stanton number due to wind gustiness and capillary wave roughness while Dalton number could be considered as constant at all wind speeds. 

Abstract IceCube is a Cherenkov detector instrumenting over a cubic kilometer of glacial ice deep under the surface of the South Pole. The DeepCore sub-detector lowers the detection energy threshold to a few GeV, enabling the precise measurements of neutrino oscillation parameters with atmospheric neutrinos. The reconstruction of neutrino interactions inside the detector is essential in studying neutrino oscillations. It is particularly challenging to reconstruct sub-100 GeV events with the IceCube detectors due to the relatively sparse detection units and detection medium. Convolutional neural networks (CNNs) are broadly used in physics experiments for both classification and regression purposes. This paper discusses the CNNs developed and employed for the latest IceCube-DeepCore oscillation measurements [1]. These CNNs estimate various properties of the detected neutrinos, such as their energy, direction of arrival, interaction vertex position, flavor-related signature, and are also used for background classification.