Meat yields and primal cut weights from beef carcasses can be predicted with similar accuracies using in-abattoir 3D measurements or EUROP classification grade.

Three-dimensional (3D) measurements extracted from beef carcass images were used to predict the weight of four saleable meat yield (SMY) traits (total SMY and the SMY of the forequarter, flank, and hindquarter) and four primal cuts (sirloin, ribeye, topside and rump). Data were collected at two UK abattoirs using time-of-flight cameras and manual bone out methods. Predictions were made for 484 carcasses, using multiple linear regression (MLR) or machine learning (ML) techniques.

Carbon Mineralization in Fractured Mafic and Ultramafic Rocks: A Review.

Mineral carbon storage in mafic and ultramafic rock masses has the potential to be an effective and permanent mechanism to reduce anthropogenic CO. Several successful pilot-scale projects have been carried out in basaltic rock (e.g.

Survival Rate of High-Rise Syndrome Cases Using Animal Trauma Triage Score in Cats.

Objective:  To assess the data of high-rise syndrome (HRS) cases and determine the relationship between Animal Trauma Triage Score (ATTS), height, injury profile, and survival rate of patients.

Study Design:  Retrospective study evaluating cats with HRS within a 4-year period.

Results:  A logistic regression analysis which included height, ground type, and ATTS variables was performed to predict survival rate of patients.

Using in-abattoir 3-dimensional measurements from images of beef carcasses for the prediction of EUROP classification grade and carcass weight.

Imaging technology can aid the automatic extraction of measurements from beef carcasses, which can be used for objective grading. Many abattoirs, however, rely on manual grading due to the required infrastructure and cost, making technology unfeasible. This study explores 3-dimensional (3D) imaging technology, requiring limited infrastructure, and its ability to predict carcass weight, conformation class and fat class for non-invasive, objective classification.