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1. Evaluation of the University of Victoria Earth System Climate Model version 2.10 (UVic ESCM 2.10)

   https://doi.org/10.5194/gmd-13-4183-2020  

   Mengis, Nadine; Keller, David P.; MacDougall, Andrew H.; Eby, Michael; Wright, Nesha; Meissner, Katrin J.; Oschlies, Andreas; Schmittner, Andreas; MacIsaac, Alexander J.; Matthews, H. Damon; et al (January 2020, Geoscientific Model Development)

   null (Ed.)

   Abstract. The University of Victoria Earth System Climate Model (UVic ESCM) ofintermediate complexity has been a useful tool in recent assessments oflong-term climate changes, including both paleo-climate modelling anduncertainty assessments of future warming. Since the last official releaseof the UVic ESCM 2.9 and the two official updates during the last decade,considerable model development has taken place among multiple researchgroups. The new version 2.10 of the University of Victoria Earth SystemClimate Model presented here will be part of the sixth phaseof the Coupled Model Intercomparison Project (CMIP6). More precisely it willbe used in the intercomparison of Earth system models of intermediatecomplexity (EMIC), such as the C4MIP, the Carbon Dioxide Removal and ZeroEmissions Commitment model intercomparison projects (CDR-MIP and ZECMIP,respectively). It now brings together and combines multiple modeldevelopments and new components that have come about since the lastofficial release of the model. The main additions to the base model are(i) an improved biogeochemistry module for the ocean, (ii) a vertically resolvedsoil model including dynamic hydrology and soil carbon processes, and (iii) arepresentation of permafrost carbon. To set the foundation of its use, wehere describe the UVic ESCM 2.10 and evaluate results from transienthistorical simulations against observational data. We find that the UVicESCM 2.10 is capable of reproducing changes in historical temperature andcarbon fluxes well. The spatial distribution of many ocean tracers,including temperature, salinity, phosphate and nitrate, also agree well withobserved tracer profiles. The good performance in the ocean tracers isconnected to an improved representation of ocean physical properties. Forthe moment, the main biases that remain are a vegetation carbon density thatis too high in the tropics, a higher than observed change in the ocean heatcontent (OHC) and an oxygen utilization in the Southern Ocean that is too low.All of these biases will be addressed in the next updates to the model. 

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2. Ionospheric Echo Detection in Digital Ionograms Using Convolutional Neural Networks

   https://doi.org/10.1029/2020RS007258  

   De La Jara, C.; Olivares, C. (August 2021, Radio Science)

   Abstract An ionogram is a graph of the time that a vertically transmitted wave takes to return to the earth as a function of frequency. Time is typically represented as virtual height, which is the time divided by the speed of light. The ionogram is shaped by making a trace of this height against the frequency of the transmitted wave. Along with the echoes of the ionosphere, ionograms usually contain a large amount of noise and interference of different nature that must be removed in order to extract useful information. In the present work, we propose a method based on convolutional neural networks to extract ionospheric echoes from digital ionograms. Extraction using the CNN model is compared with extraction using machine learning techniques. From the extracted traces, ionospheric parameters can be determined and electron density profile can be derived. 

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3. Quantum Computing in the NISQ era and beyond

   https://doi.org/10.22331/q-2018-08-06-79  

   Preskill, John (August 2018, Quantum)

   Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future. Quantum computers with 50-100 qubits may be able to perform tasks which surpass the capabilities of today's classical digital computers, but noise in quantum gates will limit the size of quantum circuits that can be executed reliably. NISQ devices will be useful tools for exploring many-body quantum physics, and may have other useful applications, but the 100-qubit quantum computer will not change the world right away - we should regard it as a significant step toward the more powerful quantum technologies of the future. Quantum technologists should continue to strive for more accurate quantum gates and, eventually, fully fault-tolerant quantum computing. 

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4. HESS Opinions: Incubating deep-learning-powered hydrologic science advances as a community

   https://doi.org/10.5194/hess-22-5639-2018  

   Shen, Chaopeng; Laloy, Eric; Elshorbagy, Amin; Albert, Adrian; Bales, Jerad; Chang, Fi-John; Ganguly, Sangram; Hsu, Kuo-Lin; Kifer, Daniel; Fang, Zheng; et al (January 2018, Hydrology and Earth System Sciences)

   Abstract. Recently, deep learning (DL) has emerged as a revolutionary andversatile tool transforming industry applications and generating new andimproved capabilities for scientific discovery and model building. Theadoption of DL in hydrology has so far been gradual, but the field is nowripe for breakthroughs. This paper suggests that DL-based methods can open up acomplementary avenue toward knowledge discovery in hydrologic sciences. Inthe new avenue, machine-learning algorithms present competing hypotheses thatare consistent with data. Interrogative methods are then invoked to interpretDL models for scientists to further evaluate. However, hydrology presentsmany challenges for DL methods, such as data limitations, heterogeneityand co-evolution, and the general inexperience of the hydrologic field withDL. The roadmap toward DL-powered scientific advances will require thecoordinated effort from a large community involving scientists and citizens.Integrating process-based models with DL models will help alleviate datalimitations. The sharing of data and baseline models will improve theefficiency of the community as a whole. Open competitions could serve as theorganizing events to greatly propel growth and nurture data science educationin hydrology, which demands a grassroots collaboration. The area ofhydrologic DL presents numerous research opportunities that could, in turn,stimulate advances in machine learning as well. 

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5. There is more than multiple choice: crowd-sourced assessment tips for online, hybrid, and face-to-face environments

   https://doi.org/10.1128/jmbe.00205-21  

   Drew, J.C. (January 2021, Journal of microbiology biology education)

   Developing effective assessments of student learning is a challenging task for faculty and even more difficult for those in emerging disciplines that lack readily available resources and standards. With the power of technology-enhanced education and accessible digital learning platforms, instructors are also looking for assessments that work in an online format. This article will be useful for all teachers, but especially for entry-level instructors, in addition to more mature instructors who are looking to become more well versed in assessment, who seek a succinct summary of assessment types to springboard the integration of new forms of assessment of student learning into their courses. In this paper, ten assessment types, all appropriate for face-to-face, blended, and online modalities, are discussed. The assessments are mapped to a set of bioinformatics core competencies with examples of how they have been used to assess student learning. Although bioinformatics is used as the focus of the assessment types, the question types are relevant to many disciplines. 

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