8 research outputs found
Causal Connections between Water Quality and Land Use in a Rural Tropical Island Watershed: Rural Tropical Island Watershed Analysis
We examined associations between riparian canopy cover, presence or absence of cattle, rainfall, solar radiation, month of year, dissolved oxygen, turbidity, salinity, and Enterococcus concentrations in riparian surface soils with Enterococcus geometric mean in-stream water concentrations at Waipā watershed on the north side of the Hawaiian island Kaua’i. Each 1% decrease in riparian canopy cover was associated with a 4.6 most probable number (MPN)/100 ml increase of the geometric mean of Enterococcus in stream water (P < 0.05). Each unit decrease in salinity (ppt) was associated with an increase of Enterococcus by 68.2 MPN/100 ml in-stream water geometric mean concentrations (P < 0.05). Month of year was also associated with increases in stream water Enterococcus geometric mean concentrations (P < 0.05). Reducing riparian canopy cover is associated with Enterococcus increases in stream water, suggesting that decreasing riparian vegetation density could increase fecal bacteria surface runoff
From Data to Software to Science with the Rubin Observatory LSST
The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset
will dramatically alter our understanding of the Universe, from the origins of
the Solar System to the nature of dark matter and dark energy. Much of this
research will depend on the existence of robust, tested, and scalable
algorithms, software, and services. Identifying and developing such tools ahead
of time has the potential to significantly accelerate the delivery of early
science from LSST. Developing these collaboratively, and making them broadly
available, can enable more inclusive and equitable collaboration on LSST
science.
To facilitate such opportunities, a community workshop entitled "From Data to
Software to Science with the Rubin Observatory LSST" was organized by the LSST
Interdisciplinary Network for Collaboration and Computing (LINCC) and partners,
and held at the Flatiron Institute in New York, March 28-30th 2022. The
workshop included over 50 in-person attendees invited from over 300
applications. It identified seven key software areas of need: (i) scalable
cross-matching and distributed joining of catalogs, (ii) robust photometric
redshift determination, (iii) software for determination of selection
functions, (iv) frameworks for scalable time-series analyses, (v) services for
image access and reprocessing at scale, (vi) object image access (cutouts) and
analysis at scale, and (vii) scalable job execution systems.
This white paper summarizes the discussions of this workshop. It considers
the motivating science use cases, identified cross-cutting algorithms,
software, and services, their high-level technical specifications, and the
principles of inclusive collaborations needed to develop them. We provide it as
a useful roadmap of needs, as well as to spur action and collaboration between
groups and individuals looking to develop reusable software for early LSST
science.Comment: White paper from "From Data to Software to Science with the Rubin
Observatory LSST" worksho
From Data to Software to Science with the Rubin Observatory LSST
editorial reviewedThe Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset will dramatically alter our understanding of the Universe, from the origins of the Solar System to the nature of dark matter and dark energy. Much of this research will depend on the existence of robust, tested, and scalable algorithms, software, and services. Identifying and developing such tools ahead of time has the potential to significantly accelerate the delivery of early science from LSST. Developing these collaboratively, and making them broadly available, can enable more inclusive and equitable collaboration on LSST science. To facilitate such opportunities, a community workshop entitled "From Data to Software to Science with the Rubin Observatory LSST" was organized by the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) and partners, and held at the Flatiron Institute in New York, March 28-30th 2022. The workshop included over 50 in-person attendees invited from over 300 applications. It identified seven key software areas of need: (i) scalable cross-matching and distributed joining of catalogs, (ii) robust photometric redshift determination, (iii) software for determination of selection functions, (iv) frameworks for scalable time-series analyses, (v) services for image access and reprocessing at scale, (vi) object image access (cutouts) and analysis at scale, and (vii) scalable job execution systems. This white paper summarizes the discussions of this workshop. It considers the motivating science use cases, identified cross-cutting algorithms, software, and services, their high-level technical specifications, and the principles of inclusive collaborations needed to develop them. We provide it as a useful roadmap of needs, as well as to spur action and collaboration between groups and individuals looking to develop reusable software for early LSST science
Causal Connections between Water Quality and Land Use in a Rural Tropical Island Watershed
Characterizing riparian ecosystems (plants, soil, and water) of Waipā, Kauaʻi of the Hawaiian Islands
Thesis (M.S.)--University of Hawaii at Manoa, 2006.Includes bibliographical references.x, 209 leaves, bound ill., maps 29 c
Recommended from our members
Causal connections between water quality and land use in a rural tropical island watershed: rural tropical island watershed analysis.
We examined associations between riparian canopy cover, presence or absence of cattle, rainfall, solar radiation, month of year, dissolved oxygen, turbidity, salinity, and Enterococcus concentrations in riparian surface soils with Enterococcus geometric mean in-stream water concentrations at Waipā watershed on the north side of the Hawaiian island Kaua'i. Each 1% decrease in riparian canopy cover was associated with a 4.6 most probable number (MPN)/100 ml increase of the geometric mean of Enterococcus in stream water (P < 0.05). Each unit decrease in salinity (ppt) was associated with an increase of Enterococcus by 68.2 MPN/100 ml in-stream water geometric mean concentrations (P < 0.05). Month of year was also associated with increases in stream water Enterococcus geometric mean concentrations (P < 0.05). Reducing riparian canopy cover is associated with Enterococcus increases in stream water, suggesting that decreasing riparian vegetation density could increase fecal bacteria surface runoff
Recommended from our members
Cross-Sectional Survey of Indicator and Pathogenic Bacteria on Vegetables Sold from Asian Vendors at Farmers’ Markets in Northern California
A cross-sectional survey was conducted during summer 2013 to determine the occurrence of Escherichia coli, fecal coliforms (FCs), E. coli O157:H7, and Salmonella on raw vegetable commodities common to Asian cuisine from 21 vendors or farmers at six farmers' markets in northern California. Based on 242 samples from six commodities (basil, yardlong beans, bitter squash, okra, squash stems and leaves, cilantro), 100% of samples had detectable FCs and 20% had detectable E. coli. The mean concentrations were 0.67 log CFU/g and 1.26 log CFU per bundle for E. coli and 4.00 log CFU/g and 6.26 log CFU per bundle for FCs. Vegetables irrigated with ground versus surface water contained lower concentrations of FCs, but this difference was not observed for E. coli. Yardlong beans, bitter squash, and okra had lower levels of FCs compared with basil, cilantro, and squash stems and leaves. Sixteen (6.6%) samples had detectable levels of Salmonella serovars (Newport, Enteritidis, Agona, and Worthington), with the majority of positives found in cilantro and squash stems and leaves. There was a twofold higher probability of Salmonella contamination in samples from growers or vendors who stated that they used organic farming practices compared with samples from those using conventional farming practices. Lastly, the concentrations of FC and E. coli bacteria were significantly associated with Salmonella contamination: for each additional 100 CFU/g or bundle, the probability of Salmonella contamination increased by ∼15 and ∼30%, respectively. None of the samples had detectable E. coli O157:H7