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Background: Characterizing principles of co-learning and stakeholder engagement for community-engaged research is becoming increasingly important. As low-income communities, Indigenous communities, and communities of color all over the world disproportionately feel the social, health, and economic impacts of environmental hazards, especially climate change, it is imperative to co-learn with these communities, so their lived experience and knowledge guide the building and sharing of a knowledge base and the development of equitable solutions. Objectives: This paper presents recent theoretical and practical support for the development of co-learning principles to guide climate adaptation and health equity innovations. We describe this development process, which included both a literature review and stakeholder engagement. The process and the resultant set of principles are relevant to community health partnerships. Adopting principles to guide design, development, and implementation prior to commencement of community health projects will help to ensure they are nonextractive and achieve maximum benefits for beneficiaries. Methods: A multiuniversity research team adopted this approach at the outset of a research endeavor in 2022. The team is currently conducting principle-based field research in non-U.S. locations where climate hazards and structural inequities have created health disparities. Conclusions: The team’s advisory board and its funder expressed enthusiasm about the development of these principles and about the prospect of Western researchers conducting a project in a way that values Indigenous and traditional communities as partners and knowledge-holders and has the potential to bring benefits to the communities involved, including increased capacity for activities promoting health, equity, and well-being. 

Abstract ALICE is a large experiment at the CERN Large Hadron Collider. Located 52 meters underground, its detectors are suitable to measure muons produced by cosmic-ray interactions in the atmosphere. In this paper, the studies of the cosmic muons registered by ALICE during Run 2 (2015–2018) are described.The analysis is limited to multimuon events defined as events with more than four detected muons (N_μ> 4) and in the zenith angle range 0° < θ < 50°. The results are compared with Monte Carlo simulations using three of the main hadronic interaction models describing the air shower development in the atmosphere: QGSJET-II-04, EPOS-LHC, and SIBYLL 2.3d.The interval of the primary cosmic-ray energy involved in the measuredmuon multiplicity distribution is about4 × 10¹⁵<E_prim< 6 × 10¹⁶eV.In this interval none of the three models is able to describe precisely the trend of the composition of cosmic rays as the energy increases. However,QGSJET-II-04 is found to be the only model capable of reproducing reasonably well the muon multiplicity distribution, assuming a heavy composition of the primary cosmic raysover the whole energy range, while SIBYLL 2.3d and EPOS-LHC underpredict thenumber of muons in a large interval of multiplicity by more than 20% and 30%, respectively.The rate of high muon multiplicity events (N_μ> 100) obtainedwith QGSJET-II-04 and SIBYLL 2.3d is compatible with the data, while EPOS-LHC produces a significantly lower rate (55% of the measured rate). For both QGSJET-II-04 and SIBYLL 2.3d, the rate is close to the data when the composition is assumed to be dominated by heavy elements, an outcome compatible with the average energy E_prim∼ 10¹⁷eV of these events.This result places significant constraints on more exotic production mechanisms.