A Comparison of the Effects of Three GM Corn Varieties on Mammalian Health

[Nathan McCorkle]

I've never heard of this journal, and their website looks like it's still in 1996.

http://www.ijbs.com/v05p0706.htm

This sentence struck me as odd, in 3D modelling parametric models are usually better...

"We applied nonparametric methods, including multiple pairwise comparisons with a False Discovery Rate approach."

--
-Nathan

[Iván Esteban Araya]

Come on O.o ... this experiment has basic mistakes on the experiment design and statical data analysis. And more important to me, where are the description of the parameters and methods they used to measured them?

[Jelmer Cnossen]

I guess the difference is that in 3D modelling, you're going the other
direction (model to world/output). In statistics or machine learning,
non-parametric models have the advantage of making fewer assumptions
on the shape of distributions, so they are considered better for some
applications.

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--
met vriendelijke groet,
Jelmer Cnossen

tel: 0648709790

[Nathan McCorkle]

On Mon, Apr 8, 2013 at 3:28 AM, Jelmer Cnossen <j.cn...@gmail.com> wrote:

I guess the difference is that in 3D modelling, you're going the other
direction (model to world/output). In statistics or machine learning,
non-parametric models have the advantage of making fewer assumptions
on the shape of distributions, so they are considered better for some
applications.

hmm, point well taken. 

[Cathal Garvey]

-----BEGIN PGP SIGNED MESSAGE-----
Hash: SHA256

"GMOs cause cancer or something" papers come out every season, and they
have no intention of being rigorously scientific, because most science
journalists are either not scientists or have been "out of the game" for
so long that they've forgotten their stats.

So, those aren't mistakes; those are deliberate lies, couched in just
enough statistics-babble to pass muster for a journo who agrees with the
basic premise and wants to say "GMOs bad, mmkay" with a citation.

Aside: "parametric" in statistics refers to a class of statistical
analyses, which may or may not be appropriate based on the dataset and
hypothesis. So, "parametric" isn't necessarily any better than
"non-parametric" in this case.

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[David Gifford]

Seemed like this paper concludes to suggest a longer term more rigorous study; I didn't see any call to ban GMO, etc etc.

On Apr 8, 2013 11:10 PM, "Cathal Garvey" <cathal...@cathalgarvey.me> wrote:

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Hash: SHA256

"GMOs cause cancer or something" papers come out every season, and they
have no intention of being rigorously scientific, because most science
journalists are either not scientists or have been "out of the game" for
so long that they've forgotten their stats.

So, those aren't mistakes; those are deliberate lies, couched in just
enough statistics-babble to pass muster for a journo who agrees with the
basic premise and wants to say "GMOs bad, mmkay" with a citation.

Aside: "parametric" in statistics refers to a class of statistical
analyses, which may or may not be appropriate based on the dataset and
hypothesis. So, "parametric" isn't necessarily any better than
"non-parametric" in this case.

[Matt Lawes]

Lol!
False crisis requires more study by concerned scientists funded to study said false crisis!

>matt

Sent from my Verizon Wireless 4G LTE DROID

-----Original message-----

[David Gifford]

What is particularly scientific about all these preconclusions?

[Matt Lawes]

Really?
Are you challenging my credentials?
Look me up on LinkedIn ..... And kiss my ---.

[David Gifford]

?? I was thinking of the green peace funded researchers

[Matt Lawes]

Well, QED then.
Hardly a neutral and dispassionate group. The sky will always be falling for Greenpeace, no matter how much environmental issues improve and despite the best of intentions.

[Patrik D'haeseleer]

I do wonder how many people here actually bothered to *read* the paper...

It's important to keep in mind that the authors of this paper were *not* responsible for the experimental design behind this data. In fact, they're reanalyzing data that they were only able to obtain by suing Monsanto, and that Monsanto had previously used to claim their GMO strains were safe.

Yes, there definitely are significant problems with the experimental design and statistical analysis used *by Monsanto*, and the authors clearly point this out. For one, Monsanto never tested a large enough number of animals and at a large enough time points and doses to have a sufficient statistical power to be able to make the claims that they did. In addition, it turns out that Monsanto didn't even consistently apply the statistical methods they described.

Lastly, the authors of this paper showed that by analyzing Monsanto's data more rigorously, they are able to detect some significant differences between the GMO and control-fed animals (mainly in those fed 1/3 of their entire diet in GMO corn), and then speculate that those differences *could* be due to residual Roundup or Bt toxin in the GMO corn.

That last part is definitely the weakest of the paper, but the main take-home message here is *not* that GMO corn is bad for your health, but that Monsanto's own data and analysis was entirely insufficient to claim safety.

(For the record - I happen to believe that the vast majority of GMO foods are actually safe. But I also happen to believe that multi-billion dollar companies like Monsanto - or drug companies - should be held to account if they do shoddy science when trying to prove that their products are safe...)

Patrik

[Nathan McCorkle]

On Tue, Apr 9, 2013 at 10:19 PM, Patrik D'haeseleer <pat...@gmail.com> wrote:

I do wonder how many people here actually bothered to *read* the paper...

It's important to keep in mind that the authors of this paper were *not* responsible for the experimental design behind this data. In fact, they're reanalyzing data that they were only able to obtain by suing Monsanto, and that Monsanto had previously used to claim their GMO strains were safe.

 :O

Thanks for pointing that out

 

Yes, there definitely are significant problems with the experimental design and statistical analysis used *by Monsanto*, and the authors clearly point this out. For one, Monsanto never tested a large enough number of animals and at a large enough time points and doses to have a sufficient statistical power to be able to make the claims that they did. In addition, it turns out that Monsanto didn't even consistently apply the statistical methods they described.

Lastly, the authors of this paper showed that by analyzing Monsanto's data more rigorously, they are able to detect some significant differences between the GMO and control-fed animals (mainly in those fed 1/3 of their entire diet in GMO corn), and then speculate that those differences *could* be due to residual Roundup or Bt toxin in the GMO corn.

It's an interesting conclusion I hadn't thought of before. It /does/ appear that the roundUp Ready gene can simply discriminate against the roundUp rather than getting rid of it.

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