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  • Elsevier  (43)
  • Periodicals Archive Online (PAO)  (7)
  • 2020-2023  (6)
  • 1950-1954  (44)
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  • 1
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    Washington : Periodicals Archive Online (PAO)
    Middle East Journal. 5 (1951) 256 
    ISSN: 0026-3141
    Topics: Ethnic Sciences , History , Political Science , Sociology , Economics
    Notes: Book Reviews
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  • 2
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    Oxford : Periodicals Archive Online (PAO)
    The British journal for the philosophy of science. 1:3 (1950:Nov.) 230 
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  • 3
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    Oxford : Periodicals Archive Online (PAO)
    The British journal for the philosophy of science. 3:[9/12] (1952:May-1953:Feb.) 82 
    ISSN: 0007-0882
    Topics: Natural Sciences in General , Philosophy
    Notes: DISCUSSIONS
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  • 4
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    Oxford : Periodicals Archive Online (PAO)
    The British journal for the philosophy of science. 3:[9/12] (1952:May-1953:Feb.) 359 
    ISSN: 0007-0882
    Topics: Natural Sciences in General , Philosophy
    Notes: NOTES AND COMMENTS
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  • 5
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    New York : Periodicals Archive Online (PAO)
    Explorations in economic history. 4:2 (1951:Dec. 15) 114 
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  • 6
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    Ann Arbor, Mich., etc., : Periodicals Archive Online (PAO)
    Journal of Asian Studies. 11:3 (1952:May) 389 
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  • 7
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    Ithaca, N.Y. : Periodicals Archive Online (PAO)
    Industrial and Labor Relations Review. 7:2 (1954:Jan.) 332 
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  • 8
    Publication Date: 2022-05-25
    Description: © The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Jacox, M. G., Alexander, M. A., Siedlecki, S., Chen, K., Kwon, Y., Brodie, S., Ortiz, I., Tommasi, D., Widlansky, M. J., Barrie, D., Capotondi, A., Cheng, W., Di Lorenzo, E., Edwards, C., Fiechter, J., Fratantoni, P., Hazen, E. L., Hermann, A. J., Kumar, A., Miller, A. J., Pirhalla, D., Buil, M. P., Ray, S., Sheridan, S. C., Subramanian, A., Thompson, P., Thorne, L., Annamalai, H., Aydin, K., Bograd, S. J., Griffis, R. B., Kearney, K., Kim, H., Mariotti, A., Merrifield, M., & Rykaczewski, R. Seasonal-to-interannual prediction of North American coastal marine ecosystems: forecast methods, mechanisms of predictability, and priority developments. Progress in Oceanography, 183, (2020): 102307, doi:10.1016/j.pocean.2020.102307.
    Description: Marine ecosystem forecasting is an area of active research and rapid development. Promise has been shown for skillful prediction of physical, biogeochemical, and ecological variables on a range of timescales, suggesting potential for forecasts to aid in the management of living marine resources and coastal communities. However, the mechanisms underlying forecast skill in marine ecosystems are often poorly understood, and many forecasts, especially for biological variables, rely on empirical statistical relationships developed from historical observations. Here, we review statistical and dynamical marine ecosystem forecasting methods and highlight examples of their application along U.S. coastlines for seasonal-to-interannual (1–24 month) prediction of properties ranging from coastal sea level to marine top predator distributions. We then describe known mechanisms governing marine ecosystem predictability and how they have been used in forecasts to date. These mechanisms include physical atmospheric and oceanic processes, biogeochemical and ecological responses to physical forcing, and intrinsic characteristics of species themselves. In reviewing the state of the knowledge on forecasting techniques and mechanisms underlying marine ecosystem predictability, we aim to facilitate forecast development and uptake by (i) identifying methods and processes that can be exploited for development of skillful regional forecasts, (ii) informing priorities for forecast development and verification, and (iii) improving understanding of conditional forecast skill (i.e., a priori knowledge of whether a forecast is likely to be skillful). While we focus primarily on coastal marine ecosystems surrounding North America (and the U.S. in particular), we detail forecast methods, physical and biological mechanisms, and priority developments that are globally relevant.
    Description: This study was supported by the NOAA Climate Program Office’s Modeling, Analysis, Predictions, and Projections (MAPP) program through grants NA17OAR4310108, NA17OAR4310112, NA17OAR4310111, NA17OAR4310110, NA17OAR4310109, NA17OAR4310104, NA17OAR4310106, and NA17OAR4310113. This paper is a product of the NOAA/MAPP Marine Prediction Task Force.
    Keywords: Prediction ; Predictability ; Forecast ; Ecological forecast ; Mechanism ; Seasonal ; Interannual ; Large marine ecosystem
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 9
    Publication Date: 2022-05-27
    Description: © The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Loranger, S., Pedersen, G., & Blomberg, A. E. A. A model for the fate of carbon dioxide from a simulated carbon storage seep. International Journal of Greenhouse Gas Control, 107, (2021): 103293, https://doi.org/10.1016/j.ijggc.2021.103293.
    Description: Offshore geological carbon storage (GCS) is a rapidly developing technology essential for meeting international climate goals. While the likelihood of leakage from a properly planned geological sequestration site is low, assurance that CO stays contained will require robust monitoring programs. While seismic imaging methods are used to monitor the geological reservoir, the ideal method for monitoring the water column above the reservoir depends on the fate and transport of CO. Whether CO is likely to be present as a rising seep of bubbles or dissolved in the water column near the seafloor will determine the appropriate monitoring technology and lead to a better understanding of the environmental impact of a potential leak. In this study, high definition video of a laboratory release of a carbon dioxide bubble seep recorded the size distribution of bubbles as a function of flow rate and orifice diameter. The transport of CO from different bubble size distributions was then modeled using the Texas A&M Oil Spill Calculator modeling suite. Model results show that the most important factor determining the rise height and transport of CO from the simulated leak was the maximum initial bubble size. For a maximum bubble radius of 5 mm, 95% of CO in the simulated seep reached a height of 17.1 m above the seafloor. When the maximum bubble radius was limited to 3 mm, 95% of CO dissolved by 7.8 m above the seafloor. The modeled results were verified during a controlled release of CO in Oslo Fjord.
    Description: This work was carried out as part of the ACT4storage project (617334) funded by Gassnova and Norwegian industry partners through the CLIMIT programme.
    Keywords: CO2, seep ; Carbon capture and storage ; Leak detection ; Numerical simulation
    Repository Name: Woods Hole Open Access Server
    Type: Article
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  • 10
    Publication Date: 2022-09-01
    Description: © The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Yang, X., Zhu, Z., Qiu, S., Kroeger, K. D., Zhu, Z., & Covington, S. Detection and characterization of coastal tidal wetland change in the northeastern US using Landsat time series. Remote Sensing of Environment, 276, (2022): 113047, https://doi.org/10.1016/j.rse.2022.113047.
    Description: Coastal tidal wetlands are highly altered ecosystems exposed to substantial risk due to widespread and frequent land-use change coupled with sea-level rise, leading to disrupted hydrologic and ecologic functions and ultimately, significant reduction in climate resiliency. Knowing where and when the changes have occurred, and the nature of those changes, is important for coastal communities and natural resource management. Large-scale mapping of coastal tidal wetland changes is extremely difficult due to their inherent dynamic nature. To bridge this gap, we developed an automated algorithm for DEtection and Characterization of cOastal tiDal wEtlands change (DECODE) using dense Landsat time series. DECODE consists of three elements, including spectral break detection, land cover classification and change characterization. DECODE assembles all available Landsat observations and introduces a water level regressor for each pixel to flag the spectral breaks and estimate harmonic time-series models for the divided temporal segments. Each temporal segment is classified (e.g., vegetated wetlands, open water, and others – including unvegetated areas and uplands) based on the phenological characteristics and the synthetic surface reflectance values calculated from the harmonic model coefficients, as well as a generic rule-based classification system. This harmonic model-based approach has the advantage of not needing the acquisition of satellite images at optimal conditions (i.e., low tide status) to avoid underestimating coastal vegetation caused by the tidal fluctuation. At the same time, DECODE can also characterize different kinds of changes including land cover change and condition change (i.e., land cover modification without conversion). We used DECODE to track status of coastal tidal wetlands in the northeastern United States from 1986 to 2020. The overall accuracy of land cover classification and change detection is approximately 95.8% and 99.8%, respectively. The vegetated wetlands and open water were mapped with user's accuracy of 94.6% and 99.0%, and producer's accuracy of 98.1% and 93.5%, respectively. The cover change and condition change were mapped with user's accuracy of 68.0% and 80.0%, and producer's accuracy of 80.5% and 97.1%, respectively. Approximately 3283 km2 of the coastal landscape within our study area in the northeastern United States changed at least once (12% of the study area), and condition changes were the dominant change type (84.3%). Vegetated coastal tidal wetland decreased consistently (~2.6 km2 per year) in the past 35 years, largely due to conversion to open water in the context of sea-level rise.
    Description: This study was supported by USGS North Atlantic Coast Cooperative Ecosystem Studies Unit (CESU) Program for Detection and Characterization of Coastal Tidal Wetland Change (G19AC00354).
    Keywords: Coastal tidal wetland ; Landsat time series ; Change detection ; Classification ; Condition change ; Cover change ; Tide ; DECODE
    Repository Name: Woods Hole Open Access Server
    Type: Article
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