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Abstract Traditional health surveillance methods play a critical role in public health safety but are limited by the data collection speed, coverage, and resource requirements. Wastewater‐based epidemiology (WBE) has emerged as a cost‐effective and rapid tool for detecting infectious diseases through sewage analysis of disease biomarkers. Recent advances in big data analytics have enhanced public health monitoring by enabling predictive modeling and early risk detection. This paper explores the application of machine learning (ML) in WBE data analytics, with a focus on infectious disease surveillance and forecasting. We highlight the advantages of ML‐driven WBE prediction models, including their ability to process multimodal data, predict disease trends, and evaluate policy impacts through scenario simulations. We also examine challenges such as data quality, model interpretability, and integration with existing public health infrastructure. The integration of ML WBE data analytics enables rapid health data collection, analysis, and interpretation that are not feasible in current surveillance approaches. By leveraging ML and WBE, decision makers can reduce cognitive biases and enhance data‐driven responses to public health threats. As global health risks evolve, the synergy between WBE, ML, and data‐driven decision‐making holds significant potential for improving public health outcomes. 

Abstract The Wombat and Giraffe kimberlite pipes in the Lac de Gras kimberlite field (64°N, 110°W) of the Northwest Territories, Canada, preserve unique post-eruptive lacustrine and paludal sedimentary records that offer rare insight into high-latitude continental paleoclimate. However, depositional timing—a key datum for atmospheric CO2 and paleoclimatic proxy reconstructions—of these maar infills remains ambiguous and requires refinement because of the large range in the age of kimberlites within the Lac de Gras kimberlite field. Existing constraints for the Giraffe pipe post-eruptive lacustrine and paludal maar sedimentary facies include a maximum Rb-Sr age of ca. 48 Ma (Ypresian, Eocene) based on kimberlitic phlogopite and a glass fission-track age of ca. 38 Ma (Bartonian, Eocene). The age of the Wombat pipe lacustrine maar sediments remains unclear, with unpublished pollen-based biostratigraphy suggesting deposition in the Paleocene (66–56 Ma). In this study, we examine distal rhyolitic tephra beds recovered from exploration drill cores intersecting the Wombat and Giraffe maar facies. We integrate zircon U-Pb laser ablation–inductively coupled plasma–mass spectrometry (LA-ICP-MS) and chemical abrasion–isotope dilution–thermal ionization mass spectrometry (CA-ID-TIMS) geochronology, glass fission-track dating, palynology, and tephra glass geochemistry to refine chronological frameworks for these sedimentary deposits. The Giraffe maar CA-ID-TIMS tephra zircon U-Pb dating yielded a Bayesian model age of 47.995 ± 0.082|0.087 Ma (Ypresian) for the upper portion of the lacustrine sediments, while a single zircon grain from tephra in the lowermost lacustrine sediments had an age of 48.72 ± 0.29|0.30 Ma. The revised geochronology for the Giraffe maar provides a working age model for the ~50 m record of lacustrine silt and indicates an age ~10 m.y. older than previously thought. The Wombat maar LA-ICP-MS zircon U-Pb dating yielded an age of 80.9 ± 1.0 Ma (Campanian), which indicates deposition during the Late Cretaceous. This first radiometric age for the Wombat maar deposits is substantially older than earlier biostratigraphic inferences of a Paleocene age. This new age suggests that the Wombat maar sediments preserve evidence of some of the oldest known freshwater diatoms and synurophytes and provide key constraints for the paleogeography of the Western Interior Seaway during the Late Cretaceous. 

Stereovision systems can extract full-field three-dimensional (3D) displacements of structures by processing the images collected with two synchronized cameras. To obtain accurate measurements, the cameras must be calibrated to account for lens distortion (i.e., intrinsic parameters) and compute the cameras’ relative position and orientation (i.e., extrinsic parameters). Traditionally, calibration is performed by taking photos of a calibration object (e.g., a checkerboard) with the two cameras. Because the calibration object must be similar in size to the targeted structure, measurements on large-scale structures are highly impractical. This research proposes a multi-sensor board with three inertial measurement units and a laser distance meter to compute the extrinsic parameters of a stereovision system and streamline the calibration procedure. In this paper, the performances of the proposed sensor-based calibration are compared with the accuracy of the traditional image-based calibration procedure. Laboratory experiments show that cameras calibrated with the multi-sensor board measure displacements with 95% accuracy compared to displacements obtained from cameras calibrated with the traditional procedure. The results of this study indicate that the sensor-based approach can increase the applicability of 3D digital image correlation measurements to large-scale structures while reducing the time and complexity of the calibration.