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Recent decades have seen an exponential rise in the application of machine learning in geoscience. Fundamental differences distinguish geoscience data from most other data types. Geoscience datasets are typically multi-dimensional, and contain 1-D (drillholes), 2-D (maps or cross-sections), and 3-D volumetric and point data (models/voxels). Geoscience data quality is a product of its resolution and the precision of the methods used to acquire it. The dimensionality, resolution, and precision of each layer within a geoscience dataset translates to limitations in spatiality, scale and uncertainty of resulting interpretations. Historically, geoscience datasets were overlaid cartographically, to incorporate subjective, experience-driven knowledge, and variances in scale, and resolution. The nuances and limitations that underpin the reliability of automated interpretation are well understood by geoscientists, but are rarely appropriately transferred to data science. However, for true integration of geoscience data, such issues cannot be overlooked without consequence. To apply data analytics to complex geoscience data (e.g., hydrothermal mineral systems) effectively, methodologies must be used that characterise the system quantitatively, using collocated analyses, at a common scale. This paper provides research and exploration insights from an innovative district-wide, scale-integrated, geoscience data project, which analysed 1,590 samples from 23 mineral deposits and prospects across the Cloncurry District, Queensland, Australia. Ten different analytical techniques, including density, magnetic susceptibility, remanent magnetisation, anisotropy of magnetic susceptibility, radiometrics, conductivity, scanning electron microscopy (SEM)-based automated mineralogy, geochemistry, and short-wave infrared (SWIR) hyperspectral data with 561 columns of scale-integrated data (+2151 columns of SWIR). All data were collected on 2 cm x 2.5 cm sample cylinders; a scale at which the confidence in coupling of data from techniques can be high. These data are integrated by design, to eliminate the need to downscale coarser measurements via assumptions, inferences, inversions, and interpolations. This scale-consistent approach is critical to the quantitative characterisation of mineral systems and has numerous applications to mineral exploration, such as linking alteration paragenesis with structural controls and petrophysical zonation. [ABSTRACT FROM AUTHOR]

The article discusses the impact of smoking to the health of the people most specially children in Hawaii. It notes that smoking causes lung cancer that lead them to death. It highlights the effort of the legislators in Hawaii headed by Governor Benjamin Cayetano, to allow the tobacco manufacturers to issue 150 million dollars in general revenue bonds to build John A. Burns School of Medicine (JABSOM) state-of-the-art medical education, research and building.

JABSOM takes its responsibility to improve health among Hawai'i's people to heart. The school's vision is, ALOHA: to Attain Lasting Optimal Health for All, a theme adopted through a strategic planning process which engaged JABSOM's partners in the health and life sciences including its private sector collaborators and its sister colleges throughout the University of Hawai'i's ten-campus system. JABSOM's ability to collaborate and contribute in these areas has been irrevocably enhanced by tobacco-related funding that the State of Hawai'i has committed to develop the Kaka'ako campus. The taxpayers' generosity has improved the education and reach of clinicians and researchers who, in turn, dedicate their lives to preventing, treating and eliminating the deadly grip tobacco holds on too many of the people of Hawai'i.