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1,291 results for “gap”
Manual soil moisture measurements from ten artificial forest gaps at the Coweeta Hydrologic Laboratory, North Carolina, 2000-2018
Ten artificial forest gaps were created in March 2002 at Coweeta, following two years of pretreatment data collection. Experimental gaps were created by pulling canopy trees with a winch until they were down. Trees, saplings, and seedlings were censused and tracked as part of a demography study. Soil moisture data was collected during the growing season as an explanatory variable for tree survivorship and mortality.
Annual measurements of vegetation canopy and basal gap intercepts from the three Conmod Pilot study locations at Jornada Basin LTER, 2008-2016
This data package contains annual measurements of vegetation canopy and basal gap sizes from transects at the Connectivity Modifier (Conmod) Pilot study on the Jornada Experimental Range from 2008-2016. There were 3 sites for this study: Gravelly Ridges, Aeolian, and Dona Ana. Within each site, there were 8 study plots, 4 of which were treatment plots where connectivity modules (conmods) were installed to decrease gap sizes between perennial vegetation. The plots were 8 x 8 meters and had an 8 x 8 meter buffer zone on both sides of the plot (upwind and downwind). Beginning in 2008, vegetation canopy and basal gap sizes were collected annually in all plots using the gap intercept method. These data were collected in 2008-2010, 2012 and 2016. At each plot, four parallel 24-meter transects crossing the upwind buffer, the plot, and the downwind buffer were measured. These parallel transects were spaced at 0.8, 2.8, 4.3, and 7.2 meter intervals across the plot and buffer areas. This study is complete (finished in 2016) and was the pilot study to the newer Cross Scale Interactions Study.
Canopy gap survey at El Verde
The survey of gaps at El Verde covers about 35 ha, including the 9 ha grid. We defined a treefall gap at El Verde as a hole in the forest canopy extending down to an average height of about 3 m or less above ground. The edge of a gap was delineated by the vertical projection of the edge of the canopy foliage. We measured the dimensions ofs present in the 35-ha study site in August 1989, one month before Hurricane Hugo. For each gap we measured L, the longest axis of the gap (distance between edges), and W, the longest axis perpendicular to L, and then approximated gap area as that of an ellipse: Area = p LW/4 (Runkle 1992). Only openings ³ 20 m2; were counted as gaps. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Gap Fire Perimeter (Santa Barbara County, CA), July 9, 2008 - From Geospatial Multi-Agency Coordination Group (GeoMAC)
The Gap Fire burned from 2008-07-01 to 2008-07-28, Lizard's Mouth area of Los Padres National Forest, Santa Barbara County. Approximately 9544 acres were burned (information per http://cdfdata.fire.ca.gov). This dataset contains a KML polygon showing the extent of the fire on 2008-07-09, and was acquired by request from the Geospatial Multi-Agency Coordination Group (GeoMAC, http://www.geomac.gov). These data are based upon input from incident intelligence sources, Global Positioning System (GPS) data, and infrared (IR) imagery. See methods for more information.
Figure 4 in Philonthus hepaticus (Coleoptera, Staphylinidae) in eastern Canada: are distribution gaps distinctive features or collecting artifacts?
Figure 4. Nearest populations to Atlantic Canada of Cicindela marginipennis, Hyperaspis troglodytes, Diomus amabilis, Ephalus latimanus, and Rypobius marinus (derived from Leonard and Bell 1999; Majka et al. 2008; Majka and Cline 2006; Majka and McCorquodale 2006; Sabine 2004).
Figure 3 in Philonthus hepaticus (Coleoptera, Staphylinidae) in eastern Canada: are distribution gaps distinctive features or collecting artifacts?
Figure 3. Distribution of Philonthus hepaticus in northeastern North America (derived from Smetana 1995).
Figure 2 in Philonthus hepaticus (Coleoptera, Staphylinidae) in eastern Canada: are distribution gaps distinctive features or collecting artifacts?
Figure 2. Distribution of Gabrius ulpius, Philonthus rufulus, and Philonthus thoracicus in northeastern North America (derived from Smetana 1995).
Figure 1 in Philonthus hepaticus (Coleoptera, Staphylinidae) in eastern Canada: are distribution gaps distinctive features or collecting artifacts?
Figure 1. Distribution of Acylophorus caseyi, Hemiquedius ferox, and Quedius s. spelaeus in northeastern North America [derived from Smetana (1971) and Moseley et al. (2006)].
Polluting the pair-instability mass gap for binary black holes through super-Eddington accretion in isolated binaries
<p>These are the results from:</p> <p>"Polluting the pair-instability mass gap for binary black holes through super-Eddington accretion in isolated binaries"<br> Authors: L.A.C. van Son, S. E. de Mink, F. S. Broekgaarden, M. Renzo, S. Justham, E. Laplace, J. Moran-Fraile, D. D. Hendriks, and R. Farmer</p> <p>ADS: https://ui.adsabs.harvard.edu/abs/2020arXiv200405187V/abstract<br> arXiv: https://arxiv.org/abs/2004.05187</p> <p>If you use (part of) these results in a scientific publication, we would greatly appreciate it if you would cite the source paper.</p> <p>This work uses <a href="https://compas.science/">COMPAS</a> to compute binary population properties (<a href="http://https://github.com/TeamCOMPAS/COMPAS/tree/master/docs">https://github.com/TeamCOMPAS/COMPAS/tree/master/docs</a>).</p> <p>*****************************</p> <p>For each of our 4 model variations (0. Fiducial, 1. Stable accretion, 2. Common envelope accretion and 3. Combined) we provide 2 files:</p> <p>1.) pythonSubmit.py file describing the initial conditions that were used to run the simulations</p> <p>2.) COMPASOutput.h5 file, which contains the following datasets resulting from our simulations :<br> ['systems',<br> 'doubleCompactObjects',<br> 'commonEnvelopes',<br> ]</p> <p>Detailed descriptions of these groups can be found in the accompanying README file.</p>
Amorphous carbon films generated through simulated deposition with GAP from 1eV to 100eV
<p>These amorphous carbon films were generated following the deposition protocol and methodology outlined by Caro et al., Phys. Rev. Lett. <strong>120</strong>, 166101 (2018) and, in more detail, in Caro et al. Phys. Rev. B <strong>102</strong>, 174201 (2020). Briefly, the structures were generated by depositing monoenergetic C atoms on a preexisting diamond (111) substrate. The interatomic potential used was the a-C GAP of Deringer and Csányi [Phys. Rev. B <strong>95</strong>, 094203 (2017)] and the molecular dynamics simulations (MD) were carried out with QUIP's GAP implementation [http://libatoms.github.io/] using LAMMPS [https://lammps.sandia.gov/] as MD engine.</p> <p>The final structures of the deposition simulations at 1, 2, 3, 4, 5, 6.5, 8, 10, 20, 60 and 100eV are provided in extended XYZ format, with obvious naming convention.</p>
Vortex input files -- Ashe et al., "Minding the data-gap trap: predicting the dynamics of abundant dolphin species under uncertainty"
<p>Vortex input file used for analyses presented in:<br> "Minding the data-gap trap: predicting the dynamics of abundant dolphin species under uncertainty", <br> by Erin Ashe, Rob Williams, Christopher Clark, Christine Erbe, Leah Gerber, Ailsa Hall, Philip Hammond, Robert C. Lacy, Randall Reeves, & Nicole Vollmer<br> </p>
Gap filled GEBA data
<p>Global (diffuse and direct) shortwave downwelling radiation at the surface between year 1961 and 2014. A total of ~1400 ground stations from GEBA archive has been selected and been through the machine learning technique "random forests" (Breiman, 2001) to fill gaps in from original GEBA dataset. A report on the gap filling process can be attained by e-mailing Trude Storelvmo (truds@uio.no).</p>
SMAP Daily Seamless Soil Moisture Products from 2015 to 2022 (Physics-constrained Gap-filling Method,PhyFill)
<p>The launch of Soil Moisture Active Passive (SMAP) satellite in 2015 has resulted in significant achievements in global soil moisture mapping. Nonetheless, spatiotemporal discontinuities in the soil moisture products have arisen due to the limitations of its orbit scanning gap and retrieval algorithms. To address this issue, this dataset presents a physics-constrained gap-filling method, shortly named PhyFill. The PhyFill method employs a partial convolutional neural network to explore spatial domain features of the original SMAP soil moisture data. Then, it incorporates variations in soil moisture induced by precipitation events and dry-down events as penalty terms in the loss function, thereby accounting for monotonicity and boundary constraints in the physical processes governing the dynamic fluctuations of soil moisture. The PhyFill model was applied to SMAP soil moisture data, resulting in continuous daily soil moisture data on a global scale. The core validation sites demonstrated that the reconstructed soil moisture data has a consistent ubRMSE compared with the original SMAP soil moisture data. The PhyFill method can generate globally continuous, high-accuracy soil moisture estimates, providing remarkable support for advanced hydrological applications, e.g., global soil moisture dry-down events and patterns.</p>
LGHAP v2: Global daily 1-km gap-free AOD grids (2002)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
LGHAP v2: Global daily 1-km gap-free AOD grids (2000)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
LGHAP v2: Global daily 1-km gap-free AOD grids (2007)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
LGHAP v2: Global daily 1-km gap-free AOD grids (2003)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
LGHAP v2: Global daily 1-km gap-free AOD grids (2009)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
LGHAP v2: Global daily 1-km gap-free AOD grids (2014)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
LGHAP v2: Global daily 1-km gap-free AOD grids (2016)
<p>A Long-term Gap-free High-resolution Air Pollutants concentration dataset (abbreviated as LGHAP) is of great significance for environmental management and earth system science analysis. In the current release of LGHAP dataset (LGHAP v2), we provide 22-year-long gap free aerosol optical depth (AOD) and near-surface PM2.5 concentrations with daily 1-km resolution covering the global land area from 2000 to 2021. Leveraging an improved big earth data analytic framework with attention-reinforced tensor construction and adaptive background information updating schemes, gap-free AOD grids were firstly derived via an integration of multimodal AODs and air quality measurements acquired from diverse satellites, ground monitors, and numerical models. For better predicting PM2.5 concentration across the globe, a scene-aware ensemble learning graph attention network (SCAGAT) was then developed to account for large modeling bias over regions with limited or even none in situ air quality measurements. These datasets were archived in the NetCDF (nc) format, while data in every year were archived as an individual submission. Python, MATLAB, R, and IDL codes were also provided to help users read and visualize the LGHAP v2 data.</p>
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