## 4 Discussion
Models including 10, 25, and 50 semilandards on a single curve, that is, without specifying the deepest point of the score profile, were evaluated showing similar general morphological trends to the seven-landmark method, but a lower differentiation power between groups (Tables 2, 4 and 5). Conversely to the seven-landmark method, these novel models are not able to distinguish between fields and hyenids, regardless of the methodological approach, namely using GM and EFA approaches. In fact, only hyenid species can be statistically differentiated when shape and size features are considered together, confirming that brown and spotted hyenas are morphologically easier to discriminate than lions and leopards, probably due to the differences in size observed between the former species (Figure S6). The second group of models based on semilandards (13, 23, and 33) was molded using two curves that end at the score profile lateral ends and the deepest point of the score cross-section. Results show that the differentiation power improves when using the two-curve models in comparison to the one-curve models, which suggests that the deepest point may be morphologically relevant to describe score morphology. Nevertheless, carnivore distinction is still less clear than using the original seven-landmark model, since the two-curve models are not able to consistently separate fields from hyenids based on the tooth scores they generate, and can only statistically distinguish between brown and spotted hyenas in form space, like the rest of the models tested in the present study (Table 6). Additionally, the EFA approach is also capable of separating the brown hyena sample from the rest of the carnivore groups included in the study when considering only shape variables (Table 7). However, this is likely due to the number of variables used in the analysis, which would result in an increase of the effects of the curse of dimensionality. In general, the results obtained here highlight that the use of high numbers of landmarks to describe tooth score cross-sections does not necessarily improve morphological characterization and carnivore differentiation. Thus, the use of semilandmark models is not recommended for the study of archaeopaelontological sites where both field and hyenid may have acted as taphonomic agents. Anyway, in case of using 2D semilandmark curves to avoid landmarking biases, researchers might want to make sure that the deepest point of the score cross-section is included in the analysis, as its location seems to be relevant for morphological characterization. Among the carnivores included in the study, hyenids seem to be easier to differentiate based on the tooth score morphology they generate, probably with reference to the effect that size has on the spotted hyena score morphology. Spotted hyenas form, indeed, the most differentiated group (see balanced accuracy in Table S2), whereas leopards usually present the lowest accuracy rates among the four groups. Thus, while the 3D GMM analysis of carnivore pits has provided useful tools for the separation of carnivores, including the distinction of leopards and lions [26], as well as wider range of carnivore taxa [20; 21; 22; 23], the GMM analysis of score morphology might be relevant for the distinction of not only hyenid species, but also different carnivore families in those scenarios, where the tooth pit sample is not large enough to perform statistical analyses due to preservation problems. Despite the positive results obtained regarding the separation among hyenid species, further analyses are required to prove the validity of the method in a more generalized context that includes a wider range of carnivore taxa. Therefore, works in this line including 2D and 3D landmark data will be conducted in the following years.