PAC_CMP: Seabed component and feature data for the continental margin of the U.S. Pacific Coast (California, Oregon, Washington) from usSEABED (pac_cmp.txt)

This data layer (PAC_CMP.txt) is one of five point coverages of known sediment samples, inspections, and probes from the usSEABED data collection for the U.S. Pacific continental margin integrated using the software system dbSEABED. This data file gives numeric data about selected components (for ex...

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Bibliographic Details
Main Authors: Jane A. Reid, Jamey M. Reid, Chris J. Jenkins, Mark Zimmermann, S. Jeffress Williams, Michael E. Field
Format: Dataset
Language:unknown
Published: USGS Science Data Catalog 2006
Subjects:
PSC
UC
Online Access:https://search.dataone.org/view/7eeb636b-51e9-49b6-9561-a36c78cdce48
Description
Summary:This data layer (PAC_CMP.txt) is one of five point coverages of known sediment samples, inspections, and probes from the usSEABED data collection for the U.S. Pacific continental margin integrated using the software system dbSEABED. This data file gives numeric data about selected components (for example, minerals, rock type, microfossils, and benthic biota) and sea floor features (for example, bioturbation, structure, and ripples) at a given site. Values in the attribute fields represent the membership to that attribute's fuzzy set. For components such as minerals, rocks, micro-biota and plants, and (or) epifauna and infauna, corals and other geologic and biologic information, the value depends on sentence structure and other components in description. For features (denoted by an '_F') such as ripples, ophiuroids, sponges, shrimp, worm tubes, lamination, lumps, grading, and (or) bioturbation, the value of the fuzzy set depends on the development of the attribute. Only the relative fuzzy presence of components and features can be determined; the absence of information does not indicate a lack of the attribute, only lack of information about that attribute. Table 5 (http://pubs.usgs.gov/ds/2006/182/table5.html) in the Larger_Work_Citation gives more information about the words or phrases that trigger each component and feature.