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We present a new compilation and analysis of broad-band ocean bottom seismometer noise properties from 15 yr of seismic deployments. We compile a comprehensive data set of representative four-component (seismometer and pressure gauge) noise spectra and cross-spectral properties (coherence, phase and admittance) for 551 unique stations spanning 18 U.S.-led experiments. This is matched with a comprehensive compilation of metadata parameters related to instrumentation and environmental properties for each station. We systematically investigate the similarity of noise spectra by grouping them according to these metadata parameters to determine which factors are the most important in determining noise characteristics. We find evidence for improvements in similarity of noise properties when grouped across parameters, with groupings by seismometer type and deployment water depth yielding the most significant and interpretable results. Instrument design, that is the entire deployed package, also plays an important role, although it strongly covaries with seismometer and water depth. We assess the presence of traditional sources of tilt, compliance, and microseismic noise to characterize their relative role across a variety of commonly used seismic frequency bands. We find that the presence of tilt noise is primarily dependent on the type of seismometer used (covariant with a particular subset of instrument design), that compliance noise follows anticipated relationships with water depth, and that shallow, oceanic shelf environments have systematically different microseism noise properties (which are, in turn, different from instruments deployed in shallow lake environments). These observations have important implications for the viability of commonly used seismic analysis techniques. Finally, we compare spectra and coherences before and after vertical channel tilt and compliance noise removal to evaluate the efficacy and limitations of these now standard processing techniques. These findings may assist in future experiment planning and instrument development, and our newly compiled noise data set serves as a building block for more targeted future investigations by the marine seismology community. [ABSTRACT FROM AUTHOR]

The Cornell Net Carbohydrate and Protein System (CNCPS) prediction of fiber digestion and microbial mass production from ruminally degraded carbohydrate has been adjusted to accommodate a ruminal N deficiency. The steps for the adjustment are as follows: 1) the ruminal available peptide and ammonia pools are used to determine the N allowable microbial growth; 2) this value is subtracted from the energy allowable microbial growth to obtain the reduction in microbial mass; 3) this mass reduction is allocated between pools of bacteria digesting fiber (FC) and nonfiber (NFC) carbohydrate according to their original proportions in the energy allowable microbial growth; 4) the reduction in fermented FC is computed as the FC bacterial mass reduction divided by its yield (g bacteria/g FC digested); and 5) this reduction is added to the FC fraction escaping the rumen. Five published studies included information that allowed us to evaluate the response of animals to added dietary N. These evaluations compared observed and CNCPS-predicted ADG with and without this adjustment. The adjustment decreased the CNCPS overprediction of ADG from 19.2 to 4.7%, mean bias declined from .16 to .04 kg/d, and the [r.sup.2] of the regression between observed and metabolizable energy (ME) or metabolizable protein allowable ADG was increased from .83 to .88 with the adjustment. When the observed dry matter intake was regressed against CNCPS-predicted DMI with an adjustment for reduction in cell wall digestibility, the [r.sup.2] was increased from .77 to .88. These results indicated the adjustment for ruminal nitrogen deficiency increased the accuracy of the CNCPS model in evaluating diets of growing animals when ruminally degraded N is deficient. Key Words: CNCPS, Microbial Growth, Modeling, Ruminal Nitrogen Deficiency

Presents a study which measured the boron concentration on the surface of silicon wafers using thermal neutron depth profiling. Experimental details; Results and discussion; Conclusions.