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The domestic dog is assumed by nearly everyone to be the consummate smeller. Within the speciesCanis familiarisindividual breeds, such as the bloodhound or beagle, are known as olfactory stars. These are “scent breeds,” a grouping variably defined as a genetic clade or breed class commonly used for scent detection tasks. Previous work suggests that the dog has a more robust olfactory anatomy than many mammal species. Now we undertake a closer investigation of the dog's olfactory system, both in relationship to its closest wild relatives, the wolf and coyote, and across individual breeds. First, we seek to resolve whether the dog has lost olfactory capacity through its domestication from the wolf lineage. Second, we test the inertial lore that among dogs, “scent breeds,” have a superior olfactory facility. As a measure of relative olfactory capacity, we look to the cribriform plate (CP), a bony cup in the posterior nasal cavity perforated by passageways for all olfactory nerve bundles streaming from the periphery to the brain. Using high‐resolution computed tomography (CT) scans and digital quantification, we compare relative CP size in 46 dog breeds, the coyote and gray wolf. Results show the dog has a reduced CP surface area relative to the wolf and coyote. Moreover, we found no significant differences between CP size of “scent” and “non‐scent” breeds. Our study suggests that the dog lost olfactory capacity as a result of domestication and this loss was not recovered in particular breed groupings through directed artificial selection for increased olfactory facility.

 

Abstract Many measurements at the LHC require efficient identification of heavy-flavour jets, i.e. jets originating from bottom (b) or charm (c) quarks. An overview of the algorithms used to identify c jets is described and a novel method to calibrate them is presented. This new method adjusts the entire distributions of the outputs obtained when the algorithms are applied to jets of different flavours. It is based on an iterative approach exploiting three distinct control regions that are enriched with either b jets, c jets, or light-flavour and gluon jets. Results are presented in the form of correction factors evaluated using proton-proton collision data with an integrated luminosity of 41.5 fb -1 at  √s = 13 TeV, collected by the CMS experiment in 2017. The closure of the method is tested by applying the measured correction factors on simulated data sets and checking the agreement between the adjusted simulation and collision data. Furthermore, a validation is performed by testing the method on pseudodata, which emulate various mismodelling conditions. The calibrated results enable the use of the full distributions of heavy-flavour identification algorithm outputs, e.g. as inputs to machine-learning models. Thus, they are expected to increase the sensitivity of future physics analyses.