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Holocene climate in the high tropical Andes was characterized by both gradual and abrupt changes, which disrupted the hydrological cycle and impacted landscapes and societies. High-resolution paleoenvironmental records are essential to contextualize archaeological data and to evaluate the sociopolitical response of ancient societies to environmental variability. Middle-to-Late Holocene water levels in Lake Titicaca were reevaluated through a transfer function model based on measurements of organic carbon stable isotopes, combined with high-resolution profiles of other geochemical variables and paleoshoreline indicators. Our reconstruction indicates that following a prolonged low stand during the Middle Holocene (4000 to 2400 BCE), lake level rose rapidly ~15 m by 1800 BCE, and then increased another 3 to 6 m in a series of steps, attaining the highest values after ~1600 CE. The largest lake-level increases coincided with major sociopolitical changes reported by archaeologists. In particular, at the end of the Formative Period (500 CE), a major lake-level rise inundated large shoreline areas and forced populations to migrate to higher elevation, likely contributing to the emergence of the Tiwanaku culture. 

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.