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Category: Science

If you can find Nemo, the reef is already dying

The Atlantic has an article about intact vs. overfished coral reefs.

[L]arge predators both reflect and safeguard the health of coral reefs. If they’re fished out, the rippling consequences can be devastating, leading to fewer fish and sicklier corals. And since those changes happened decades ago, they’ve influenced our perceptions of what coral reefs should look like. We think of the kaleidoscopic realms of Pixar movies or aquarium tanks, but those are reefs that have already been badly depleted. Pristine ones are worlds where predators abound, and colorful prey cower within the coral. “It’s like the difference between the English countryside and the African Serengeti,” […]

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When the ice melts, what does that look like?

Drum-Heller-Channels by User:Woofles

National Geographic’s Glenn Hodges explains the Channeled Scablands of Washington State, with some quite awesome photos by Michael Melford.

In the middle of eastern Washington, in a desert that gets less than eight inches of rain a year, stands what was once the largest waterfall in the world. It is three miles wide and 400 feet high—ten times the size of Niagara Falls—with plunge pools at its base suggesting the erosive power of an immense flow of water.

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Before Code, there was the Codex

Nautilus has an article by Philip Auerswald, Author of The Code Economy: A Forty-Thousand-Year History. Auerswald tries to tie our current practice of crystallising rules in Code back to the Codexes and Recipes of older times, and sees our civilisation as a system of dealing with complexity by packaging and encapsulating it. According to Auerswald, running Code on machines is new, previously we have been running it on humans:

“Code” as I intend it incorporates elements of computer code, genetic code, cryptologic code, and other forms as well. But, as I describe in my book The Code Economy: A Forty-Thousand Year History, published this year, it also stands as its own concept—the algorithms that guide production in the economy—for which no adequate word yet exists. Code can include instructions we follow consciously and purposively, and those we follow unconsciously and intuitively.

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Magic circles banning autonomous cars

Trapping Autonomous Cars

Somebody sent me a link to Vice withe the comment “A multiple hit in the Venn Diagram of your interests”.

It’s about an artist using technology disguised as ritual magic to trap self-driving cars (and similar shenanigans). The assessent was correct, this is beautiful.

The image from the article shown above shows a self-driving car inside fake street markings. The broken lines allow the cars logic to enter the circle, the unbroken linkes mark a demarcation that must not be crossed, hence the car can never leave.

It ties back to a story my driving instructor told me. He was making a point about “How things are being presented matters”, relating about a beginners driver who had been told to imagine unbroken lines as a “wall that cannot be crossed” and who because of that had problems – sometimes rules must be broken to preserve their meaning and spirit.



Awesome underwater maps of the Indian Ocean, thanks to MH 370

Geological Insights from Malaysia Airlines Flight MH370 Search

»The tragic disappearance of Malaysia Airlines flight MH370 on 8 March 2014 led to a deep-ocean search effort of unprecedented scale and detail. Between June 2014 and June 2016, geophysical survey teams aboard ships used echo sounding techniques to create state-of-the-art maps of the seafloor […] of the southeastern Indian Ocean.

[…] Previous ocean floor maps in this region had an average spatial resolution (pixel size) of more than 5 square kilometers, but the new maps resolve features smaller than 0.01 square kilometer (an area slightly larger than a soccer field).«

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The GRIM test and data in scientific papers

The GRIM test is very simple. The acronym stands for “granularity-related inconsistency of means (GRIM) test” – it evaluates whether reported averages can be made out of their reported sample sizes, and it works on integer data and small N.

Here is how this works:

Let’s make a pretend sample of twelve undergraduates, with ages as follows:


The average age is 20.92 (2dp), and we run the experiment on a Monday.

However, the youngest person in our sample is about to turn 18. At midnight, their age ticks over, […]

[W]e run the experiment again on Tuesday. Now our has the following age data:


The average age is 21 exactly.

Now, consider this: the sum of ages just changed by one unit, which is the smallest amount possible. It was 251 (which divided by 12 is 20.92), and with the birthday of the youngest member, became 252 (which divided by 12 is 21 exactly).

So if the mean cannot be the product of a division by 12, the data must be fake. The authors collected 260 phsychology papers and checked the reported stats to see if the results are even possible. Many are not.


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