Changes

As part of the suite of Conductivity, Temperature, Depth (CTD) AI tools being produced by the Office of the Chief Data Steward (OCDS), we are developing a model to assist with identifying and deleting poor quality scans during the CTD quality control process. Using a combination of a Gaussian Mixture Model (GMM) to cluster CTD scans into groups with similar physical properties and Multi-Layer Perceptrons to classify the scans in each group, we are able to automatically flags the poor-quality scans to be deleted with a high degree of accuracy. Through the deployment of the model as a real-time online endpoint and the support of model communication through a client-side program, we have successfully integrated an experimental model into the client's business process in a field testing environment. The continuation of this line of work will now look to bring the model into a production environment for regular usage in the quality control process.

As part of the suite of Conductivity, Temperature, Depth (CTD) AI tools being produced by the Office of the Chief Data Steward (OCDS), we are developing a model to assist with identifying and deleting poor quality scans during the CTD quality control process. Using a combination of a Gaussian Mixture Model (GMM) to cluster CTD scans into groups with similar physical properties and Multi-Layer Perceptrons to classify the scans in each group, we are able to automatically flag the poor-quality scans to be deleted with a high degree of accuracy. Through the deployment of the model as a real-time online endpoint and the support of model communication through a client-side program, we have successfully integrated an experimental model into the client's business process in a field testing environment. The continuation of this line of work will now look to bring the model into a production environment for regular usage in the quality control process.

== Use Case Objectives ==

== Use Case Objectives ==