Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab2 Valdes-Donoso CaliforniaAg2019 73-3.jpg|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Bispo-Silva Geosciences23 13-11.png|240px]]</div>
'''"[[Journal:Costs of mandatory cannabis testing in California|Costs of mandatory cannabis testing in California]]"'''
'''"[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Geochemical biodegraded oil classification using a machine learning approach]]"'''


Every batch of [[wikipedia:Cannabis|cannabis]] sold legally in California must be tested for more than 100 contaminants. These contaminants include 66 pesticides, for 21 of which the state's tolerance is zero. For many other substances, tolerance levels are much lower than those allowed for food products in California. This article reviews the state's testing [[Regulatory compliance|regulations]] in context—including maximum allowable tolerance levels—and uses primary data collected from California's major cannabis testing [[Laboratory|laboratories]] and several cannabis testing equipment manufacturers, as well as a variety of expert opinions, to estimate the cost per pound of testing under the state's framework. We also estimate the cost of collecting [[Sample (material)|samples]], which depends on the distance between cannabis distributors and laboratories. We find that, if a batch fails mandatory tests, the value of cannabis that must be destroyed accounts for a large share of total testing costs, more than the cost of the tests that laboratories perform. Findings from this article will help readers understand the effects of California's testing regime on the price of legal cannabis in the state, and understand how testing may add value to products that have passed a series of tests that aim to validate their safety. ('''[[Journal:Costs of mandatory cannabis testing in California|Full article...]]''')<br />
[[Chromatography|Chromatographic]] oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of [[convolutional neural network]]s (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The [[open-source software]] Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... ('''[[Journal:Geochemical biodegraded oil classification using a machine learning approach|Full article...]]''')<br />
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Revision as of 13:37, 13 May 2024

Fig1 Bispo-Silva Geosciences23 13-11.png

"Geochemical biodegraded oil classification using a machine learning approach"

Chromatographic oil analysis is an important step for the identification of biodegraded petroleum via peak visualization and interpretation of phenomena that explain the oil geochemistry. However, analyses of chromatogram components by geochemists are comparative, visual, and consequently slow. This article aims to improve the chromatogram analysis process performed during geochemical interpretation by proposing the use of convolutional neural networks (CNN), which are deep learning techniques widely used by big tech companies. Two hundred and twenty-one (221) chromatographic oil images from different worldwide basins (Brazil, USA, Portugal, Angola, and Venezuela) were used. The open-source software Orange Data Mining was used to process images by CNN. The CNN algorithm extracts, pixel by pixel, recurring features from the images through convolutional operations ... (Full article...)
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