What follows is the continuation, in serial form, of a central chapter from my book A Primer in the Art of Deception: The Cult of Nuclearists, Uranium Weapons and Fraudulent Science.
SCAM NUMBER FIFTEEN: Rely on your models to create reality.
When radiation is liberated into the environment, either from an accident at a nuclear power plant or the incineration of a uranium weapon, where does the radiation go? Given sufficient motivation, time and money, radiation monitors can be dispatched into the field and laboriously map in what direction the radiation traveled, where it was deposited, and the number of people potentially exposed. Depending on the radioisotopes involved and the level of contamination drifting through the air or settling to contaminate potable water sources and agricultural products, very fuzzy estimates can be manufactured of what the possible external and internal dosages to the members of the exposed population might be. Assembling the picture of what has occurred is arduous and the outcome is, at best, an educated guess. Until an easy and rapid method is devised and implemented for measuring external exposure history and internal contamination among large numbers of people, actual dosages of an at-risk population are not ascertainable. Because we cannot/do not measure where all those radioactive atoms go and who accumulates them and in what concentrations, and because radiation biology is still in its infancy in determining the full range of biological effects produced in those contaminated, there is tremendous uncertainty as to the outcome of a radiation release on public health. The powers that be will adamantly deny this assessment.
As an alternative to actually going out to measure and map the full consequences of an environmental release of radiation, methods have been developed to “model” what might actually be taking place. For such a model to have any validity, a number must first be derived representing the dose of radiation received by each member of the exposed population. Once again, “averaging” is the relied upon methodology. An average dose is postulated for an average individual within the exposed group. Among the factors taken into account in deriving this number are the radioisotopes involved, the type of radiation these emit, the energy transmitted by the radiation, the external hazard from gamma emitters, the organ(s) of retention of internal emitters, and the residency time of the internal emitters within the various organs of the body. The total energy the average person is thought to have received is stated in a unit of measure called the “person-rem” or “person-Sievert.” This number is then multiplied by the number of people in the exposed population to derive the “collective dose.” By multiplying this number by the appropriate risk factor(s) [to be explained later], a quick estimate can be derived of how many cancers and of what type are likely to develop in the exposed population over their lifetime as a result of the collective exposure. This methodology has great utilitarian value because it can very rapidly provide a rough guess of the possible health consequences from routine or accidental emissions.
Needless to say, the concept of collective dose, similar to that of the concept of dose, is vulnerable to a range of abuses from mild massaging to gross misrepresentation. Scams, identical to the ones previously mentioned, can be applied to the concept of collective dose to paint the desired image of the consequences of a radiation release. The assumptions made by researchers in their determination of such variables as the amount of radiation released, the average dose, or number of people exposed can sculpt the derived collective dose into any number of different guises.
The idea of collective dose is grounded in the very abstract and dubious notion that such a thing as an average dose can be derived that faithfully represents the exposure received by each member of a population. Implicit in the concept is the assumption that radiation is uniformly distributed, that no hotspots develop that enhance exposure to local groups, that dietary habits are similar throughout the population and all members ingest similar diets that contain equivalent quantities of radiocontaminants. Perhaps more importantly, the concept of collective dose assumes uniform vulnerability to radiation injury, failing to take into account the heightened vulnerability of such subgroups as women, children, fetuses, those who are genetically predisposed to above average radiosensitivity and people with compromised immune systems. This is analogous to the hot particle problem where some cells in an organ receive no hits and a few receive huge numbers of hits, for many in a population may receive no exposure while some small fraction may receive a highly significant dose. Averaging the radiation over the whole population may have the effect of understating the health impact.
To predict the number of cancers likely to be induced in the exposed population, the collective dose is multiplied by the appropriate risk factor(s) published by the radiation protection agencies. These risk factors are derived from the rates of cancer observed in epidemiological studies that have achieved consensual acceptance by the radiation protection community, such as the corrupted Hiroshima study, and from the model of dose-response favored by whoever is doing the predicting. Radiation protection agencies currently rely on the Linear No-Threshold Hypothesis to develop risk factors and to predict the incidence of cancer in the aftermath of a radiation release.
The reason for the vehement clash over the shape of the dose-response curve in the low-dose range can now be readily understood. The model creates reality. The model chosen to represent the human organism’s response to radiation controls the prediction of the public health impact of a radiation release. The number of people known to develop cancer as the result of a radiation accident is largely determined by the model chosen to evaluate the event. Radiogenic cancers can take as long as decades to develop after exposure. These cancers become masked by normal incidence rates of the disease. Normal fluctuations from year to year in cancer deaths and the number of cases of new cancers can disguise the contribution played by a radiation event in sickening the population. Epidemiological studies have the potential of providing answers, but they are challenging to design and implement so as to deliver unambiguous results. To fill the knowledge void, researchers and public health officials turn to the currently accepted models to explain the health implications of an incident. Knowledge of the radioisotopes released, their quantities, and the number of people exposed are all that is necessary to do the math to determine the number of cancers likely to be produced. The answer may have little to do with what is actually but invisibly taking place in the population. But that doesn’t matter. Here and now, all that matters is what people believe is happening. The truth, if it is rigorously pursued, won’t be known for decades. The models relied upon to interpret events forge the perception.
To date, the collective dose from radiation incidents has been filtered through the Linear No-Threshold Hypothesis to derive the cancer consequences of the events. The Cult of Nuclearists can no longer allow this conservative and precautionary approach to craft the public’s perception when it concerns mass exposure of populations to low doses of radiation. The LNTH produces the unwelcome prediction that cancers inevitably will be produced. This conclusion is repellent and an obstacle to the public acquiescing to the proliferation of uranium weapons, nuclear bunker-busters, small fourth-generation fusion weapons, and a resumption in nuclear testing at the Nevada Test Site. What can be done to alter public opinion? The answer is obvious. Change your model. And that is exactly what is being done through the current push to discredit the LNTH and replace it with a model that postulates that low-dose exposure is without hazard.