 Good afternoon. I am Pilar Escriva, a researcher at Departamento de Prehistoria, Arqueología Historia Antiga, at Universidad de Valencia, Spain. Our research is focused on the origin and evolution of early agricultural societies. Ceramic is a key aspect to our research. Characterization of ceramic has evolved, and further aspects have been added to analysis. To name a few, technology, raw materials, or decoration tools. The ability of these new methods has to be proven. In the next few minutes, I will show you how we tested our own method to analyze neolithic ceramic from a technological point of view. Well, I hear you, and my communication is some different. More focused in artifact reporting than selection. But this is what I have. Sorry. Over the years, handcrafted neolithic pottery has been studied in many different ways, trying to solve the complexity of non-standorized material products as opposed to other subsequent productions like ever. We propose a global vision for selection and analysis of this type of material culture, where the three main characteristics of ceramic vessel I include, typology, technology, and decoration. Our goal is to define a scientific method focused on technology, thus being rational, analytic, and precise, and verifiable, that will enable such integral analysis. Our technological assessment is measured by an index named PTI, or Production Task Index. Method is described by Mike Clure, a former researcher at our working group in 2004. This index evaluates the amount of labor investment. The higher the PTI, the larger labor spend to craft the vessel. Index is built by aggregating rates from some production items into one single number now as PTI. Such items are here, you can see the table with the numbers and the items. Texture, inclusion size, sorting, inclusion frequency, and surface treatment, internal and external. As a result of our experience, we decide that the slip and decoration here, these items were not to be included in our PTI, but assessed and scored separately. Moreover, some other items are evaluated, but not aggregate at our PTI, such as thickness of walls, baking environment, vacuums, and density, for example. A brief description and point values are shown for a better understanding. I don't have time. Sorry, but if you want something, Barbara has my name to question. Our PTI rates from 3 here. You can see the fresh cut of the pottery. And the very least labor, 226 here, is the very most labor. Ho-test method, worthiness of our method, was tested by comparing two double blind researchers to the same sample of pottery. It was also compared the difference between bay eye or microscopic observation. All gathered data was converted into quantitative data, which enables all kinds of statistical treatment. In addition to the archaeological information provided by the study as such, our working method has been further improved in such a way it is homogeneous, standardized, and repotensible. The sample is obtained from Misenna, Arthur Millenian-calibrated BC site in Valencia, Spain, with 322 fragments. For this comparison, only fragments, which were part of a same vessel, are eligible. That makes a total of different vessels in the sample, named MMV here, or minimum number of vessels of 175, mostly undecorated. All these data are collected in a customized database where archaeological heritage useful information is stored. Fragments that cannot be defined at that level will not be compared at PTI calculation. Nevertheless, those fragments are recorded in order to pass database as well. These are the main features of site of Misenna. It's a 20,000 square meter open-air Neolithic village. A number of negative structures were developed. Siloes and pipes where fragments were found. These structures were quickly fulfilled. Mostly materials are in these structures again, and only the material which archaeological info was taken and stored. In this case, only 322 fragments of 1,000 and 1,000 founds in this site. Well, two researchers separately got each fragment and assigned point value at each item. Range of point values is shown in brackets here, here, and here. Absolute difference per item was calculated. Out of six items, it was observed that three items show the most similar results. Internal treatment, external treatment, and texture. Blue and yellow areas show absolute difference between researchers 1 and 2 of 0 and 1. Here, 0 and 1 is the same color for all the cheese. It is above 50% of fragments where point value were assigned exactly the same. It's above 95% where difference is 0 or 1. On the other hand, it was also found that absolute difference in assigning point value was significantly different in three items, size, temper, and sorting. Less than 30% is zero difference, and less than 8% is 0 or 1. Why? Well, you can see this area and this one in red. Gojin point values in these three last items is based in the predominant amount of particles found in a fresh cut of pottery. This predominant amount of particles might be perceived as particles with larger number here, very little, but a lot of them, or with larger surface in the cut. Here, in the red cycle, we can see these bigger surfaces. Here, with an example of this situation, and criteria to identify predominant particles had not been properly set. We can talk about that, but I think the problem is set criteria between researchers. These curves saw the amount of vessels with PTI aggregated points per item, the observer 1 green line, and observer 2 this line. To finish the data processing, we compare the absolute difference results with distance and P values with Kolmogorov-Smirnov test. And results of tests are consistent with absolute difference as shown before. We have a distance between both curves of 0,35 in this test. Then it's fair to say those curves are very much alike, but not enough to be statistically equal. Here with a summary of problems found and how to solve them. Here is the problem about the item and how to solve that. We are quite satisfied. We identified some potentially problems, and we found how to solve them. Regarding the temperature size and sorting, we found the different criteria to identify predominant particles we have shown again. And how to solve improved definition of this item learned by the example with a photo book of ceramics and ceramics complicated. And don't use different tools, bear-eye or microscope. About the temper amount, the point values not properly recorded in database because the database is very complicated. And the solution is easy adaptation and simplification of databases. And different criteria to identify predominant particles. We have to set criteria and use a double-check technique. It's regarding two times, each number, each word, with typing to assure is the correct. About the treatment internal and external when erosion, gorging criteria are not properly defined. And if the pottery is erosionated, maybe it's better don't make this test. And texture, about the texture is a problem because when you see the pottery, you cut a little fragment to study. And the researcher that cut this fragment fills the density of fragments. And the second researcher don't. And maybe only the solution we proposed is item to be gorged only by the researcher who actually cut the fragment. Conclusions. Well, about our research model, the pros are the method is fully operational, a scientific model. The study of technology improves information of vessels about the production types and operational change, and can be better characterized. And the model is especially useful for non-standard pottery, like prehistoric. But the cons, in the other hand, the time consumption and database complexity are increased. We thought research on different collection has to be comparable. Methods that look comparable and quantitative has to be tested to make sure there really are. And I hope you found this study interesting. And behalf of our team of University of Valencia, I would like to thank you for your attention and the session and the congress organizers. Thank you very much. Thank you.