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Find video protocols related to scientific articles indexed in Pubmed.
Observations on abundance of bluntnose sixgill sharks, Hexanchus griseus, in an urban waterway in Puget Sound, 2003-2005.
PLoS ONE
PUBLISHED: 01-01-2014
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The bluntnose sixgill shark, Hexanchus griseus, is a widely distributed but poorly understood large, apex predator. Anecdotal reports of diver-shark encounters in the late 1990's and early 2000's in the Pacific Northwest stimulated interest in the normally deep-dwelling shark and its presence in the shallow waters of Puget Sound. Analysis of underwater video documenting sharks at the Seattle Aquarium's sixgill research site in Elliott Bay and mark-resight techniques were used to answer research questions about abundance and seasonality. Seasonal changes in relative abundance in Puget Sound from 2003-2005 are reported here. At the Seattle Aquarium study site, 45 sixgills were tagged with modified Floy visual marker tags, along with an estimated 197 observations of untagged sharks plus 31 returning tagged sharks, for a total of 273 sixgill observations recorded. A mark-resight statistical model based on analysis of underwater video estimated a range of abundance from a high of 98 sharks seen in July of 2004 to a low of 32 sharks seen in March of 2004. Both analyses found sixgills significantly more abundant in the summer months at the Seattle Aquarium's research station.
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What is Visualize?

JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

How does it work?

We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

Video X seems to be unrelated to Abstract Y...

In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.