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ddPCR

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LoD is the lowest analyte concentration likely to be reliably distinguished from the LoB and at which detection is feasible. LoD is determined by utilising both the measured LoB and test replicates of a sample known to contain a low concentration of analyte.

LoB is the highest apparent analyte concentration expected to be found when replicates of a blank sample containing no analyte are tested.

LoB = meanblank + 1.645(SDblank)

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Paying attention to the qualifications of the author(s)/composer(s) is another crucial role in crap detection at it will help discern whether or not to take the piece seriously or to use it for further research.

In the Rheinghold text , he explains the importance of pay attention the website layout as well as content. However, in doing so, you must tune your crap detection and remember that not everything with a fancy layout is reliable, and vice versa.

Rather than simply stating information, the author (Berger) explains his source and the way in which he broke his research down into smaller categories. This citation is also apart of crap detection with a reliable source.

The implementing of this specific link gives important attribution and increases source reliability. The text makes a statement and is able to back it up with an external, secure source.

Though Rheinghold stresses the importance of crap detection and researching your sources, he accepts the fact that there a limits that we reach in terms of discernment of validity. This is shown as the ISIS busters reach ethical and practical limits of search. It is important in the way that one mustn't get overwhelmed with finding the true source origin because you can only go so far.

Rheinghold stresses the importance of looking at the base of things, rather than simply the makeup and what you see initially, it is important to dig deeper and look at sources from a questionable yet structured angle.

The article ends in 'edu' which, as Rheinghold states, increases estimation of its credibility.

Considering that the origin of this post comes from a non-secure site, that appears a tad amateur - also brings forth speculation. It is a blog site, and considering this - I somehow take what is posted 'with a grain of salt'.

I begin critiquing this article based on Rheinghold's initial conversation with his daughter. In the text Rheinghold suggests using a free internet service - Whois , in order to search for validity in research. After plugging this domain name into the site, I find that the name of the registered owner is 'Educase'. Educase is a nonprofit core data service for research and analysis.

I will be connecting this text through Howard Rheinghold's "Crap Detection 101" from chapter 2 of his book Net Smart - How to Thrive Online. This allows for further critic of this article in terms of this theme.

视频烟雾检测研究进展

Does anyone have an authoritative link for this concept of zones and how they work? It'd be much appreciated.

Early event detection problems can go here. Two example cases just came to my mind are: 1- in emergency response: detecting a disaster quickly is important. 2- in computational journalism: many locals suddenly start talking about an event means something newsworthy is going on.

This will be an ongoing challenge for species distribution modeling, because most of the data appropriate for these purposes is not collected in such a way as to allow the straightforward application of standard detection probability/occupancy models. This could potentially be addressed by developing models for detection probability based on species and habitat type. These models could be built on smaller/different datasets that include the required data for estimating detectability.

Presentation summarizing an approach to duplicate web page detection that was developed by a researcher whilst at Google in the early 2000s

Useful recap of LSH

Recent survey paper for hashing-based approaches to similarity search

This paper has a very useful overview of previous work that is worth reading under section 9.

Datasets used are DBPL, ENRON, UNIREF-4GRAM. All small (<1M records) in web terms and I would guess, all with small document sizes.

Given a lengthy paper, could potentially divide into smaller documents (1 doc === 1 page) and do signature calculation on a per-page basis. This could have the benefit of bounding the search time by limiting the number of pages that need to be rendered to text in order to start the lookup process.