The threshold concept still holds in tox except when the EPA is on a tear.
A note from Steve about one hit getting shelved for the short-term, now that unethical human experiments need to be covered and excused.
https://twitter.com/JunkScience/status/451697944589123584/photo/1
One hit came from old junk science in radiation biophysics we talked about here at junk science many times. Our favorite detective is the great toxicologist who fights the one hit junkers, Ed Calabrese. Ed is the enemy of Linear modeling with no threshold, and shows in his research at low levels there is a hormetic effect from what may be toxins at higher exposures, but that makes sense if you believe that the dose makes the poison.
As Rod would say, but now you’re entering the Twilight Zone of EPA one hit junk science. Ed tries to show how bad it is and the Stern an Muller were involved in scientific misconduct, motivated by the anti nuc attitudes.
http://junkscience.com/?s=ed+CALABRESE
One hit is so good for the nannies and the promoters of the precautionary principle.
Calabrese showed that Stern and Muller lied about radiation biophysics and no threshold, and the EPA and other, as far up as the National Academy of Sciences, promote one hit–which allows the EPA to turn the Clean Air Act and other pollution/tox statutes on their head.
They identify a tox based on big dose rat and mouse studies and then go out and identify it with very sensitive monitors and then regulate it arbitrarily with idea there is no safe level for toxic or carcinogenic effect. So instead of safe air or water, the EPA can regulate to create no pollutants and since that is impossible they just get to tighten standards when they need something to do.
And of course they insist they are saving lives.
Add Gene to the list of potential contributors!
My goodness Gene, between you and Tadchem you’re making me feel like i need to get a brain injection.
Another Ed, Edwin T. Janyes, wrote a book on probability as extended logic that everyone working in or around science should know about. The first three chapters of it are available online here: http://bayes.wustl.edu/etj/prob/book.pdf
Full version here: http://web.archive.org/web/20001201214000/http://bayes.wustl.edu/etj/prob.html
It is a book about inference. Not only it is immensely useful; it is also very well written. Apropos toxicity, Jaynes writs In the preface (which is more than accessible to general public):
“What is ‘safe’? We are not concerned here only with abstract issues of mathematics and logic. One of the main practical messages of this work is the great effect of prior information on the conclusions that one should draw from a given data set. Currently much discussed issues such as environmental hazards or the toxicity of a food additive, cannot be judged rationally if one looks only at the current data and ignores the prior information that scientists have about the phenomenon. This can lead one to greatly overestimate or underestimate the danger.”
“A common error, when judging the effects of radioactivity or the toxicity of some substance, is to assume a linear response model without threshold (that is, without a dose rate below which there is no ill effect). Presumably there is no threshold effect for cumulative poisons like heavy metal ions (mercury, lead), which are eliminated only very slowly if at all. But for virtually every organic substance (such as saccharin or cyclamates), the existence of a finite metabolic rate means that there must exist a finite threshold dose rate, below which the substance is decomposed, eliminated, or chemically altered so rapidly that it has no ill effects. If this were not true, the human race could never have survived to the present time, in view of all the things we have been eating.”
…
“Kilodose effects are irrelevant because we do not take kilodoses; in the case of a sugar substitute the important question is: What are the threshold doses for toxicity of a sugar substitute and for sugar, compared to the normal doses? If that of a sugar substitute is higher, then the rational conclusion would be that the substitute is actually safer than sugar, as a food ingredient. To analyze one’s data in terms of a model which does not allow even the possibility of a threshold effect, is to prejudge the issue in a way that can lead to false conclusions however good the data. If we hope to detect any phenomenon, we must use a model that at least allows the possibility that it may exist.”