If I add a new class with the exact same axiom.
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The both classes do not work. Only the last one. The class work separately but not when other similar classes exist. What do I wrong?
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Is it possible? Or have I misunderstood how to use axioms in classes or should I test the class in another way? Learn more.
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Viewed 76 times. Examples of classes and their conditions: All cities located north from the start city. I just want to use the axioms in the classes to do this. Sandberg Sandberg 1 3 3 bronze badges. Using markdown for formatting makes it also much more readable. And adding images directly instead of links Sorry, can't add pictures directly to the page as I don't have enough 'points' new. Sign up or log in Sign up using Google. Sign up using Facebook. In an interview to Retraction Watch , the project members have said:.
The group are now writing up a paper with an analysis of these results. But until then, readers can follow the action over at the COMPare blog. Last month a paper on the role of pigeons as trainable observers of pathology and radiology breast cancer images was published in PLOS ONE. Among other things, the authors of the paper, Richard M. Levenson , Elizabeth A. Krupinski, Victor M.
Navarro, and Edward A. Wasserman, were interested to find out whether pigeons could be trained to discriminate between benign and malignant pathology and radiology images. The objective is not to rely on pigeons for clinical diagnostic support, but rather to promote the use of pigeons as an appropriate animal model for human observers in medical image perception studies.
In particular, the constantly updated medical image recognition and display technologies must be validated by sometimes expensive and hard-to-reach trained observers. The authors of this paper suggest that trained pigeons could be used as a cost-effective, tractable, relevant, informative, and statistically interpretable surrogate for human observers in order to help determine the reliability of these new technologies.
The research was in part motivated by other recent studies reporting that pigeons are pretty comparable to humans at discriminating in other areas. For example, studies have reported that pigeons can distinguish the paintings of Monet from Picasso. Also, studies have reported that pigeons can distinguish human male from female faces. The results of this paper are consistent with these findings. After training, the pigeons were able to distinguish benign from malignant human breast histopathology and the presence of microcalcifications on mammogram images but had difficulty evaluating the malignant potential of detected breast masses.
The pigeon performance here corresponds closely to human performance. Granting these results, however, pigeons might still not be very good models for human observers in these areas, since pigeons might be achieving comparable results to humans here but by entirely different means. For example, it seems that the way in which pigeons discriminate human male and female faces is largely texture-based. The authors acknowledge this problem and try to alleviate it by offering some evidence of mechanisms.
Because of considerations such as these, this paper seems to highlight quite nicely the role of different sorts of evidence in determining whether a particular animal model is appropriate for a particular task.
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In order to argue that the pigeon is an appropriate model for human observers here, the authors provide both evidence that the pigeon performs similarly to humans in the relevant observation tasks and evidence that this similar performance is attributable to similar underlying mechanisms. The next issue of Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences is currently in progress, but some articles from the forthcoming issue have recently been made available online first.
One article is by Alexander R. Federica Russo Philosophy, Amsterdam and Jon Williamson Philosophy, Kent have argued that, at least in the health sciences, establishing a causal claim typically requires both evidence that there exists an appropriate difference-making relationship and evidence that there exists an appropriate mechanism to explain this difference-making relationship, where this difference-making relationship may be a statistical association between the putative cause and effect , Interpreting causality in the health sciences, International Studies in the Philosophy of Science , 21 2 , pp.
This claim that establishing a causal claim in the health science typically requires these two types of evidence has become known as the Russo-Williamson thesis.
They clarify the thesis by defining difference-making evidence as exposure—outcome evidence, and by distinguishing three sub-types of mechanistic evidence in the health sciences, viz. They conclude as follows:. From this perspective, we find that both mechanistic evidence and exposure—outcome evidence are largely comprised of observed associations between variables, and this leads to further clarifications about the meanings of these evidence types and how each should be evaluated.