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as it indicates fusion performance. To optimize Tritium consumption and limit neutron activation, a support function included in the plasma control system called a dud detector will trigger an alarm if the plasma fails to achieve expected fusion performance. This function has been developed and routinely employed at JET during DT campaigns. This study presents machine-learning methods based on a surrogate model of the neutron rate, which can be used as an advanced dud detector. In preparation for DT operations in BEST, HL-3, ITER, and SPARC, we investigate the portability and inherent limitations of these ML methods by analysing similar DT experiments conducted at TFTR.