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   Gokcumen, Omer (June 2018, American Anthropologist)
2. Extreme events driving year-to-year differences in gross primary productivity across the US

   https://doi.org/10.5194/bg-18-6579-2021  

   Turner, Alexander J.; Köhler, Philipp; Magney, Troy S.; Frankenberg, Christian; Fung, Inez; Cohen, Ronald C. (January 2021, Biogeosciences)

   Abstract. Solar-induced chlorophyll fluorescence (SIF) has previously been shown to strongly correlate with gross primary productivity (GPP); however this relationship has not yet been quantified for the recently launched TROPOspheric Monitoring Instrument (TROPOMI). Here we use a Gaussian mixture model to develop a parsimonious relationship between SIF from TROPOMI and GPP from flux towers across the conterminous United States (CONUS). The mixture model indicates the SIF–GPP relationship can be characterized by a linear model with two terms. We then estimate GPP across CONUS at 500 m spatial resolution over a 16 d moving window. We observe four extreme precipitation events that induce regional GPP anomalies: drought in western Texas, flooding in the midwestern US, drought in South Dakota, and drought in California. Taken together, these events account for 28 % of the year-to-year GPP differences across CONUS. Despite these large regional anomalies, we find that CONUS GPP varies by less than 4 % between 2018 and 2019. 

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3. Historic trends and sources of year‐over‐year stability in Montana winter wheat yields

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   Lachowiec, Jennifer; Berg, James_E; Liang, Meng; Correr, Fernando_H (April 2023, Crop Science)

   Abstract Producers desire cultivars that consistently perform with high yields and end‐use qualities. Unlike easily recognized average yield improvements, yield stability over time is less examined, especially when considering the role of breeding relative to other factors like management and changing climatic conditions. Our study system was a 70‐year historical dataset from which we estimated the year‐over‐year stability of winter wheat (Triticum aestivumL.) cultivars released by Montana's Agricultural Experimental Station. We examined yield stability within six locations representing diverse growing conditions across Montana and found no evidence that breeding has improved stability, that stability may be decreasing over time at one location, and that the year‐over‐year stability of a cultivar is sensitive to location. We examined the role of climatic conditions, including temperature, and rainfall to understand if increased climatic variability was masking improved patterns of stability. However, the lack of impact of breeding remained. These findings suggest that Montana's winter wheat may benefit from selective breeding for increased stability within locations. 

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4. A year of internal Poincaré waves in southern Lake Michigan: YEAR OF LM P WAVES

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   Choi, Jun; Troy, Cary D.; Hsieh, Tsung-Chan; Hawley, Nathan; McCormick, Michael J. (July 2012, Journal of Geophysical Research: Oceans)
5. A Year in K-12 AI Education

   https://doi.org/10.1609/aimag.v40i4.5289  

   Touretzky, David; Gardner-McCune, Christina; Breazeal, Cynthia; Martin, Fred; Seehorn, Deborah (January 2020, AI Magazine)

   The time is ripe to consider what 21st-century digital citizens should know about artificial intelligence (AI). Efforts are under way in the USA, China, and many other countries to promote AI education in kindergarten through high school (K–12). The past year has seen the release of new curricula and online resources for the K–12 audience, and new professional development opportunities for K–12 teachers to learn the basics of AI. This column surveys the current state of K–12 AI education and introduces the work of the AI4K12 Initiative, which is developing national guidelines for AI education in the USA.   A Note to the Reader This is the inaugural column on AI education. It aims to inform the AAAI community of current and future developments in AI education. We hope that the reader finds the columns to be informative and that they stimulate debate. It is our fond hope that this and subsequent columns inspire the reader to get involved in the broad field of AI education, by volunteering their expertise in their local school district, by providing level-headed input when discussing AI with family and friends or by lending their considerable expertise to various decision makers. We welcome your feedback, whether in the form of a response to an article or a suggestion for a future article. – Michael Wollowski, AI in Education Column Editor 

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