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In the Southern California desert, the Salton Sea is the cause of a local socioenvironmental crisis reflective of various environmental injustices. Today’s Salton Sea is fed primarily through agricultural water run-off and effluent discharge. Over 23% of the Latinx and Torres Martinez Desert Cahuilla Indian identifying residents live below the poverty line in the two zip codes north of the sea (US Census Bureau, 2021). Persistent droughts and inequitable policies have accelerated the sea’s evaporation, exacerbating environmental health problems such as poor air quality and respiratory illnesses like asthma (Farzan et al., 2019). Since 2010, nonprofit organizations such as Alianza Coachella Valley (hereafter referred to as Alianza) have been addressing these issues and campaigning for economic and environmental justice in the Eastern Coachella Valley (ECV). Prior to this work, no reliable and continuous source of water quality information was easily accessible to local communities. Most recently, Alianza championed a community science initiative with the goal of establishing ongoing environmental monitoring, research, and advocacy. Through this initiative, community members formed the Salton Sea Environmental Time Series (SSET) in 2021 with support from the American Geophysical Union’s (AGU’s) Thriving Earth Exchange program. This collaboration, along with a variety of other institutions, has fostered diversity, equity, and inclusion (DEI) within scientific academia for underrepresented ECV scholars and can serve as a blueprint for future initiatives. 

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.