Abstract: A mismatch between model-based classifications produced by a first version of a machine learning threat discernment model and a second version of a machine learning threat discernment model for a file is detected. The mismatch is analyzed to determine appropriate handling for the file, and taking an action based on the analyzing. The analyzing includes comparing a human-generated classification status for a file, a first model version status that reflects classification by the first version of the machine learning threat discernment model, and a second model version status that reflects classification by the second version of the machine learning threat discernment model. The analyzing can also include allowing the human-generated classification status to dominate when it is available.

Abstract: A mismatch between model-based classifications produced by a first version of a machine learning threat discernment model and a second version of a machine learning threat discernment model for a file is detected. The mismatch is analyzed to determine appropriate handling for the file, and taking an action based on the analyzing. The analyzing includes comparing a human-generated classification status for a file, a first model version status that reflects classification by the first version of the machine learning threat discernment model, and a second model version status that reflects classification by the second version of the machine learning threat discernment model. The analyzing can also include allowing the human-generated classification status to dominate when it is available.

Abstract: A mismatch between model-based classifications produced by a first version of a machine learning threat discernment model and a second version of a machine learning threat discernment model for a file is detected. The mismatch is analyzed to determine appropriate handling for the file, and taking an action based on the analyzing. The analyzing includes comparing a human-generated classification status for a file, a first model version status that reflects classification by the first version of the machine learning threat discernment model, and a second model version status that reflects classification by the second version of the machine learning threat discernment model. The analyzing can also include allowing the human-generated classification status to dominate when it is available.

Abstract: A mismatch between model-based classifications produced by a first version of a machine learning threat discernment model and a second version of a machine learning threat discernment model for a file is detected. The mismatch is analyzed to determine appropriate handling for the file, and taking an action based on the analyzing. The analyzing includes comparing a human-generated classification status for a file, a first model version status that reflects classification by the first version of the machine learning threat discernment model, and a second model version status that reflects classification by the second version of the machine learning threat discernment model. The analyzing can also include allowing the human-generated classification status to dominate when it is available.

Abstract: A mismatch between model-based classifications produced by a first version of a machine learning threat discernment model and a second version of a machine learning threat discernment model for a file is detected. The mismatch is analyzed to determine appropriate handling for the file, and taking an action based on the analyzing. The analyzing includes comparing a human-generated classification status for a file, a first model version status that reflects classification by the first version of the machine learning threat discernment model, and a second model version status that reflects classification by the second version of the machine learning threat discernment model. The analyzing can also include allowing the human-generated classification status to dominate when it is available.

Abstract: Technology is described for an electromagnetic apparatus and system that sorts different electrically conductive metals. In one example, an electrodynamic sorting circuit includes a wire-wound, gapped, core (WWGC) and a capacitor bank. The WWGC includes a magnetic core including a gap, and an electrical conductor coiled around the magnetic core. A current in the electrical conductor is configured to generate a magnetic field in the magnetic core and the gap. The capacitor bank is coupled in series with the electrical conductor of the WWGC. Various other circuitries, systems, devices, components, and methods are also disclosed.

Abstract: A mismatch between model-based classifications produced by a first version of a machine learning threat discernment model and a second version of a machine learning threat discernment model for a file is detected. The mismatch is analyzed to determine appropriate handling for the file, and taking an action based on the analyzing. The analyzing includes comparing a human-generated classification status for a file, a first model version status that reflects classification by the first version of the machine learning threat discernment model, and a second model version status that reflects classification by the second version of the machine learning threat discernment model. The analyzing can also include allowing the human-generated classification status to dominate when it is available.

Abstract: A fluid-sparged helical channel reactor can include a constrained flow unit located within a reactor body. The unit has an inner wall and an outer wall which produces a helical constrained flow along a substantially enclosed helical flow path around an axial interior volume. At least part of the outer wall includes a sparging portion to allow fluid reactant to be sparged into the helical constrained flow. A liquid inlet fluidly connected to the reactor body and configured to allow addition of a liquid into the enclosed helical flow path. A sparging fluid inlet is fluidly connected to the reactor body which supplies a sparging fluid to the sparging portion of the constrained-flow unit. A liquid outlet fluidly is connected to the reactor body to allow removal of liquid from the constrained-flow unit. A gas outlet is fluidly associated with the enclosed helical flow path to allow removal of gases from the enclosed helical flow path.

Abstract: A method of reacting compounds can include directing a liquid into a helical constrained flow (37) having an inner circumferential flow surface and an outer circumferential flow surface. The helical constrained flow (37) can be formed around an axial interior volume (38). At least a portion of the helical constrained flow can be exposed to a sparging portion (35) to allow a fluid to be sparged into the liquid along the helical constrained flow (37). The fluid reactant can be sparged through the helical constrained flow so as to form a fluid product.

Abstract: A method of reacting compounds can include directing a liquid into a helical constrained flow (37) having an inner circumferential flow surface and an outer circumferential flow surface. The helical constrained flow (37) can be formed around an axial interior volume (38). At least a portion of the helical constrained flow can be exposed to a sparging portion (35) to allow a fluid to be sparged into the liquid along the helical constrained flow (37). The fluid reactant can be sparged through the helical constrained flow so as to form a fluid product.