| 1 |
Random Forests  |
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| 2 |
Seasonal Variations of Surface fCO2 and Sea-Air CO2 Fluxes in the Ulleung Basin of the East/Japan Sea  |
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| 3 |
Climate change. Dr. Cool.  |
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de Boyer Montegut, C., G. Madec, A.S. Fischer, A. Lazar and D.N. Iudicone, 2004. Mixed layer depth over the global ocean:An examination of profile data and a profile-based climatology. Journal of Geophysical Research-Oceans, 109(C12): C12003. |
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Dickson, A.G., C.L. Sabine and J.R. Christian, 2007. Guide to Best Practices for Ocean CO2 measurements, 1-196 pp. |
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Empirical methods for the estimation of Southern Ocean CO~2: support vector and random forest regression  |
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| 7 |
An observation of primary production enhanced by coastal upwelling in the southwest East/Japan Sea  |
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| 8 |
Approximation capabilities of multilayer feedforward networks  |
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| 9 |
Estimation of Fugacity of Carbon Dioxide in the East Sea Using In Situ Measurements and Geostationary Ocean Color Imager Satellite Data  |
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| 10 |
Long-term trend of CO2 and ocean acidification in the surface water of the Ulleung Basin, the East/Japan Sea inferred from the underway observational data  |
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| 11 |
Monthly measured primary and new productivities in the Ulleung Basin as a biological "hot spot" in the East/Japan Sea  |
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| 12 |
Recent variability of the global ocean carbon sink  |
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Muller, A. and S. Guido, 2017. Introduction to Machine Learning with Python. O’Rielly. |
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Na, J.-Y., J.-W. Seo and S.-K. Han, 1992. Monthly mean sea surface winds over the adjacent seas of the Korea Peninsula. J. Oceangr. Soc. Korea, 27: 1-10. |
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Estimating temporal and spatial variation of ocean surface pCO2 in the North Pacific using a self-organizing map neural network technique  |
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| 16 |
Oh, D.-C., 1998. A study on the characteristics of fCO2 distributions and CO2 flux at the air-sea interface in the seas around Korea. MS Thesis Seoul National University, 105 p. |
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| 17 |
The Air-Sea Exchange of CO~2 in the East Sea (Japan Sea)  |
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| 18 |
Large accumulation of anthropogenic CO~2 in the East (Japan) Sea and its significant impact on carbonate chemistry (DOI 10.1029/2005GB002676)  |
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| 19 |
Park, S., T. Lee and Y.-H. Jo, 2016. Sea Surface pCO2 and Its Variability in the Ulleung Basin, East Sea Constrained by a Neural Network Model. The Sea, 21(1): 1-10. |
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| 20 |
Scikit-learn: Machine Learning in Python  |
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| 21 |
Recommendations for autonomous underway pCO 2 measuring systems and data-reduction routines  |
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| 22 |
Seasonal Variation of CO~2 and Nutrients in the High-Latitude Surface Oceans: A Comparative Study  |
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| 23 |
Takahashi, T., S. Sutherland and R. Wanninkhof, 2009. Climatological mean and decadal change in surface ocean pCO2, and net sea-air CO2 flux over the global oceans. Deep-Sea Research, 56(8-10): 554-577. |
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| 24 |
Estimating the monthly pCO2 distribution in the North Atlantic using a self-organizing neural network  |
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| 25 |
Vapnik, V., 2000. The Nature of Statistical Learning Theory. 2nd ed., Springer, New York. |
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Relationship between wind speed and gas exchange over the ocean  |
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| 27 |
Wanninkhof, R., 2014. Relationship between wind speed and gas exchange over the ocean revisited. Limnol. Oceanogr.:Methods, 12(6): 351-362. |
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Yoo, S. and J. Park, 2009. Why is the southwest the most productive region of the East Sea/Sea of Japan?, Journal of Marine Systems, 78(2): 15-15. |
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| 29 |
A Global Surface Ocean fCO~2 Climatology Based on a Feed-Forward Neural Network  |
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