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Who conducts biological research, where they do it and how results are disseminated vary among geographies and identities. Identifying and documenting these forms of bias by research communities is a critical step towards addressing them. We documented perceived and observed biases in movement ecology, a rapidly expanding sub-discipline of biology, which is strongly underpinned by fieldwork and technology use. We surveyed attendees before an international conference to assess a baseline within-discipline perceived bias (uninformed perceived bias). We analysed geographic patterns inMovement Ecologyarticles, finding discrepancies between the country of the authors’ affiliation and study site location, related to national economics. We analysed race-gender identities of USA biology researchers (the closest to our sub-discipline with data available), finding that they differed from national demographics. Finally, we discussed the quantitatively observed bias at the conference, to assess within-discipline perceived bias informed with observational data (informed perceived bias). Although the survey indicated most conference participants as bias-aware, conversations only covered a subset of biases. We discuss potential causes of bias (parachute-science, fieldwork accessibility), solutions and the need to evaluate mitigatory action effectiveness. Undertaking data-driven analysis of bias within sub-disciplines can help identify specific barriers and move towards the inclusion of a greater diversity of participants in the scientific process. 

We present a parallelized optimization method based on fast Neural Radiance Fields (NeRF) for estimating 6-DoF pose of a camera with respect to an object or scene. Given a single observed RGB image of the target, we can predict the translation and rotation of the camera by minimizing the residual between pixels rendered from a fast NeRF model and pixels in the observed image. We integrate a momentum-based camera extrinsic optimization procedure into Instant Neural Graphics Primitives, a recent exceptionally fast NeRF implementation. By introducing parallel Monte Carlo sampling into the pose estimation task, our method overcomes local minima and improves efficiency in a more extensive search space. We also show the importance of adopting a more robust pixel-based loss function to reduce error. Experiments demonstrate that our method can achieve improved generalization and robustness on both synthetic and real-world benchmarks. 

Abstract First reported in the 1960s, offshore freshened groundwater (OFG) has now been documented in most continental margins around the world. In this review we compile a database documenting OFG occurrences and analyze it to establish the general characteristics and controlling factors. We also assess methods used to map and characterize OFG, identify major knowledge gaps, and propose strategies to address them. OFG has a global volume of 1 × 10⁶ km³; it predominantly occurs within 55 km of the coast and down to a water depth of 100 m. OFG is mainly hosted within siliciclastic aquifers on passive margins and recharged by meteoric water during Pleistocene sea level lowstands. Key factors influencing OFG distribution are topography‐driven flow, salinization via haline convection, permeability contrasts, and the continuity/connectivity of permeable and confining strata. Geochemical and stable isotope measurements of pore waters from boreholes have provided insights into OFG emplacement mechanisms, while recent advances in seismic reflection profiling, electromagnetic surveying, and numerical models have improved our understanding of OFG geometry and controls. Key knowledge gaps, such as the extent and function of OFG, and the timing of their emplacement, can be addressed by the application of isotopic age tracers, joint inversion of electromagnetic and seismic reflection data, and development of three‐dimensional hydrological models. We show that such advances, combined with site‐specific modeling, are necessary to assess the potential use of OFG as an unconventional source of water and its role in sub‐seafloor geomicrobiology. 

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.