Search PubMed⌕ Search
Home/Archive/PubMed

PubMed · 5901355

The UCLA MEDLARS computer system.

F J Garvis
View original source ↗Export metadata ↓

Abstract

Under a subcontract with UCLA the Planning Research Corporation has changed the MEDLARS system to make it possible to use the IBM 7094/7040 direct-couple computer instead of the Honeywell 800 for demand searches. The major tasks were the rewriting of the programs in COBOL and copying of the stored information on the narrower tapes that IBM computers require. (In the future NLM will copy the tapes for IBM computer users.) The differences in the software required by the two computers are noted. Major and costly revisions would be needed to adapt the large MEDLARS system to the smaller IBM 1401 and 1410 computers. In general, MEDLARS is transferrable to other computers of the IBM 7000 class, the new IBM 360, and those of like size, such as the CDC 1604 or UNIVAC 1108, although additional changes are necessary. Potential future improvements are suggested.

Explore related subjects

CaliforniaComputersInformation SystemsLibraries, MedicalJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

F J Garvis. 1966. The UCLA MEDLARS computer system.. https://pubmed.ncbi.nlm.nih.gov/5901355/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2026

Coding-complete genome sequence of grapevine leafroll-associated virus 13 from grapevine in California.

In this study, we report the coding-complete genome sequence of Grapevine leafroll-associated virus 13 (GLRaV-13), isolate CA8881, detected in Vitis vinifera in California, USA. The genome sequence exhibited over 95% nucleotide identity with previously reported GLRaV-13 isolates and contributed to better understanding of the genetic diversity of ampeloviruses infecting grapevine.

California↗
PubMed2000

Assessing water quality impacts and cleanup effectiveness in streams dominated by episodic mercury discharges.

Accurate pollutant mass budgets are needed for identifying contaminant sources and establishing cleanup goals. We monitored mercury discharges from an abandoned mine site in northern California with the objectives of: (1) estimating the mass loading of mercury from the site; (2) evaluating the factors that control the mercury discharges; (3) assessing the significance of peak flows in transporting contaminants; and (4) developing methods for measuring the effectiveness of cleanup efforts. We sampled water downstream from the mine site over a wide range of streamflows. Mercury concentrations varied over 2000-fold, from 485 to 1040000 ng/l, grossly exceeding the regulatory water quality objective of 12 ng/l at all times. Particulate mercury represented over 99.97% of the total mercury, and mercury concentrations were closely correlated to suspended sediment concentrations (r = 0.98). Thus, we can use suspended sediment concentrations as a proxy for mercury concentrations, and calculate a continuous record of mercury flux from continuous monitoring of streamflow (using a small flume) and turbidity (using an optical backscatter sensor). Mercury fluxes inferred in this way are consistent with fluxes estimated from field samples. In January and February of 1998, our small abandoned mine site released approximately 82 kg of mercury to downstream waters. Most of the mercury was released during brief intense rainstorms. For example, in one 200-min period we recorded 3.4 cm of rain, a 2.6-fold increase in streamflow (460-1120 l/s), and an 82-fold increase in mercury flux (1.2-99 g/min). Over 75% of the total mercury flux during this 2-month period occurred in less than 10% of the total time. In systems such as this one, where contaminant transport is highly episodic, sampling programs that miss the high-flow episodes may greatly underestimate the actual water quality threat. In addition, changes in pollutant fluxes or concentrations in receiving waters may not reflect changes in pollutant sources (such as an environmental cleanup) if the stochastic forcing (e.g. intense rainstorms) varies through time. We propose that water quality trends can be more accurately measured by changes in the relationship between contaminant flux and stochastic driving factors, as expressed by contaminant rating curves.

California↗