This paper presents the SHADOW team's submission to the ASVSpoof 2024 challenge. We evaluated various models, including ECAPA-TDNN, ResNet34, ConvNeXt, and S4 Structured-State-Space Models. 2D convolution-based models outperformed other types, with the best Progress set results achieved using FwSE-ResNet34 with codec augmentations. In the Track 1 Eval set, this system achieved minDCF=0.44, a 47\% improvement over the challenge baseline. For Track2, we contribute a straightforward method for combining well-calibrated speaker and spoofing detection scores into a single system. This involves calculating the posterior probability for a trial being both same-speaker and bonafide. However, the significant mismatch between the Dev and Progress/Eval sets not only complicated the selection of the best systems and codecs but also impacted the Eval set calibration and score combination. Nevertheless, we achieved a-DCF=0.397 in Track 2, a 42% improvement over the baseline.