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How Are Q Scores Calculated?

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When celebrities go off the deep end, we’ll hear folks say they've “damaged their Q rating.” What does that really mean? What is a Q Score in the first place? Let’s take a look at the mysterious numbers.

How are Q Scores calculated?

The methodology behind the mysterious ratings is actually fairly straightforward. Marketing Evaluations, Inc. polls a representative national sample about performers, brands, TV shows, or some other property. First, it asks respondents if they’ve heard of the performer or show. If a respondent has heard of the relevant entity, the survey asks if they would rate it poor, fair, good, very good, or one of their favorites.


From there, calculating the Q Score is just a quick bit of division. Divide the percentage of people who peg a performer or show as one of their favorites by the percentage of people who have heard of the entity in the first place, drop the decimal points, and you’ve got your Q Score.

For example, according to a 1992 New York Times story, Jaleel White – yep, Steve Urkel – was the day’s top dog when it came to Q Score, narrowly edging out incumbent champ Bill Cosby. While only 53 percent of all respondents were familiar with White, 26 percent of all respondents listed White as one of their favorite performers, which racked up a stout Q Score of 49.

What does the Q stand for?

This simple division is also what gives the score its name. The “Q” stands for “quotient.”

Why do networks and advertisers care so much about favorites?

For all the reasons you’d guess. The Q Scores website explains that consumers or viewers who are exposed to one of their favorite performers or personalities are more likely to be attentive, will have higher recall, and will retain a more positive image of the brand, product, or show in question.

Is there just one Q Score?

Not even close. Marketing Evaluations, Inc. maintains several different Q Score databases, including ones for personalities (both living and dead), sports personalities, characters and licensed properties, TV shows, and consumer brands. There’s also a Cartoon Q that polls a nationwide group of 1800 kids every six months. These databases can be further sorted by age, income level, and similar variables.

Who uses these numbers?

Advertisers and media execs pay big cash for access to the Q Score database. Apparently the numbers can really come in handy when it’s time to cast a new show or sign a new spokesperson.

Any oddball firsts on the list of Q Scores?

Definitely. In August 2000 IBM announced that its chess-playing supercomputer Deep Blue had become the first computer to earn a Q Score. Interestingly, Deep Blue was still on the national radar even three years after the computer’s highly publicized victory over chess Grandmaster Gary Kasparov. IBM’s press release announcing the score revealed that Deep Blue had earned a Q Score of 9, which put the computer in the same league as Larry King, Carmen Electra, Howard Stern, and Carson Daly. (Since we know you were wondering – that figure also put Deep Blue a tick ahead of Gilbert Gottfried and Count Chocula.)

Even better for IBM, Deep Blue’s score put the computer well ahead of the 6 rating earned by Oracle CEO Larry Ellison and Sun Microsystems co-found Scott McNealy.

Who else has a particularly high Q Score?

CNN’s coverage of Deep Blue’s pioneering Q Score in 2000 listed a few other people and characters with particularly high ratings. At the time, Albert Einstein boasted a stout 56, Mickey Mouse was good for a 44, and Elvis’ Q Score was a decidedly not-dead 33.

What’s the best way to crater your Q Score?

© GARY I ROTHSTEIN/epa/Corbis

Ask LeBron James. After the basketball star announced he was leaving Cleveland for Miami in his televised “The Decision” special last summer, his Q Score plummeted from 24 to 14. The good news for LeBron is that his Q Score has recovered slightly; reports from last month indicated that his score had crept its way back up to 17.

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iStock // Ekaterina Minaeva
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technology
Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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Cs California, Wikimedia Commons // CC BY-SA 3.0
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science
How Experts Say We Should Stop a 'Zombie' Infection: Kill It With Fire
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Cs California, Wikimedia Commons // CC BY-SA 3.0

Scientists are known for being pretty cautious people. But sometimes, even the most careful of us need to burn some things to the ground. Immunologists have proposed a plan to burn large swaths of parkland in an attempt to wipe out disease, as The New York Times reports. They described the problem in the journal Microbiology and Molecular Biology Reviews.

Chronic wasting disease (CWD) is a gruesome infection that’s been destroying deer and elk herds across North America. Like bovine spongiform encephalopathy (BSE, better known as mad cow disease) and Creutzfeldt-Jakob disease, CWD is caused by damaged, contagious little proteins called prions. Although it's been half a century since CWD was first discovered, scientists are still scratching their heads about how it works, how it spreads, and if, like BSE, it could someday infect humans.

Paper co-author Mark Zabel, of the Prion Research Center at Colorado State University, says animals with CWD fade away slowly at first, losing weight and starting to act kind of spacey. But "they’re not hard to pick out at the end stage," he told The New York Times. "They have a vacant stare, they have a stumbling gait, their heads are drooping, their ears are down, you can see thick saliva dripping from their mouths. It’s like a true zombie disease."

CWD has already been spotted in 24 U.S. states. Some herds are already 50 percent infected, and that number is only growing.

Prion illnesses often travel from one infected individual to another, but CWD’s expansion was so rapid that scientists began to suspect it had more than one way of finding new animals to attack.

Sure enough, it did. As it turns out, the CWD prion doesn’t go down with its host-animal ship. Infected animals shed the prion in their urine, feces, and drool. Long after the sick deer has died, others can still contract CWD from the leaves they eat and the grass in which they stand.

As if that’s not bad enough, CWD has another trick up its sleeve: spontaneous generation. That is, it doesn’t take much damage to twist a healthy prion into a zombifying pathogen. The illness just pops up.

There are some treatments, including immersing infected tissue in an ozone bath. But that won't help when the problem is literally smeared across the landscape. "You cannot treat half of the continental United States with ozone," Zabel said.

And so, to combat this many-pronged assault on our wildlife, Zabel and his colleagues are getting aggressive. They recommend a controlled burn of infected areas of national parks in Colorado and Arkansas—a pilot study to determine if fire will be enough.

"If you eliminate the plants that have prions on the surface, that would be a huge step forward," he said. "I really don’t think it’s that crazy."

[h/t The New York Times]

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