首页|Studies from University of Quebec Trois-Rivieres Provide New Data on Machine Lea rning (Assessing the potential responses of ten important fisheries species to a changing climate with machine learning and observational data across the provin ce ...)
Studies from University of Quebec Trois-Rivieres Provide New Data on Machine Lea rning (Assessing the potential responses of ten important fisheries species to a changing climate with machine learning and observational data across the provin ce ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in artific ial intelligence. According to news reporting out of Quebec, Canada, by NewsRx e ditors, research stated, "Models are needed to predict changes in game fish abun dances with respect to climatic factors undergoing change, but such models are o ften limited by data availability and the capacity of statistical methods to fit chAllenging ecological datasets." Our news journalists obtained a quote from the research from University of Quebe c Trois-Rivieres: "We use current methods in machine learning to describe the re sponses of ten fish species to climatic factors across Quebec. We assembled a ne w province-wide, synthetic dataset of fish catches spanning almost 50 years and 6000 sites. Extreme Gradient Boosting (XGBoost) models revealed that climatic fa ctors are more important predictors of trends in game fish catches than nuisance factors (sampling gear, time), lending support to collating other heterogeneous datasets for analyses. Mean annual temperature and precipitation were the most important drivers of species catches."
University of Quebec Trois-RivieresQue becCanadaNorth and Central AmericaCyborgsEmerging TechnologiesMachine Learning
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