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A clip-level label might say a doorbell occurs somewhere in a recording, without saying when. Such a label is weak for sound-event detection because the desired output is a timeline of events. DCASE challenge work has used weakly labeled real audio alongside strongly labeled synthetic data to train event detectors. A model can predict frame-level scores, aggregate them to match clip labels and then threshold or smooth scores into events. That learning process can discover useful time regions, but the clip label alone cannot correct every wrong boundary. There are several ways a system can fail. It may predict the sound too early or too late, merge two separate rings into one event, or detect a background cue that often accompanies the event. Two sounds can overlap, so a multi-label timeline may be needed. A strong clip-classification score can coexist with poor localization if the model hears the right class but assigns it to the wrong time. Evaluation should therefore include independently annotated onsets and offsets or another task-appropriate strong reference. Weak labels are attractive because people can tag a short recording faster than marking every event boundary. Yet annotation quality and coverage still matter: “no bell” may mean no annotator noticed a faint bell, not that it was absent. A model trained on domestic rooms may struggle with public transport or factory acoustics. Test false alarms, missed events and timing tolerance separately; the allowed timing error must match the application. A home-notification product and an acoustic research dataset may value different response delays. Human review can improve training by correcting high-uncertainty segments, while simulated mixtures can supply precise time labels with a risk of synthetic-to-real shift. Preserve the audio and annotation provenance. Weakly supervised detection is a useful route toward temporal predictions, not a declaration that clip-level tags were secretly exact timestamps all along.