Abstract: Disclosed is a lidded magazine (100) for holding plural generally flat sample collection cards (10) in spaced parallel relation, the magazine is open at its top and includes upstanding opposed side walls (110,120), each wall (110,120) further including plural opposed pairs of inwardly extending wall ribs (112,122) spaced along each wall (110,120) to provide therebetween card accepting slots, a first slot dimension (W) being defined by the spacing between an interior face (154) of each wall, the magazine being characterised in that the walls (110,120) at the top (152) of each slot (150) are tapered outwardly such that the first dimension (W) is greatest at the top of the slot (150).

Abstract: Systems and methods for automated objective identification are disclosed herein. The system can include a user device and a memory. The memory can include a filed database including field data associated with previously identified objectives. The memory can include a scoring database containing a machine-learning scoring algorithm. The system can include at least one server that can receive an identifier of a content portion, extract field information identifying at least one attribute of the identified content portion from the content portion, input extracted field information into the machine-learning scoring algorithm; receive identification of objectives forming a set of objectives from the machine-learning scoring algorithm; sort the objectives forming the set of objectives according to scores generated for each of the objectives in the set of objectives; and output the sorted objectives to the user device.

Abstract: Systems and methods for automated objective identification are disclosed herein. The system can include a user device and a memory. The memory can include a filed database including field data associated with previously identified objectives. The memory can include a scoring database containing a machine-learning scoring algorithm. The system can include at least one server that can receive an identifier of a content portion, extract field information identifying at least one attribute of the identified content portion from the content portion, input extracted field information into the machine-learning scoring algorithm; receive identification of objectives forming a set of objectives from the machine-learning scoring algorithm; sort the objectives forming the set of objectives according to scores generated for each of the objectives in the set of objectives; and output the sorted objectives to the user device.