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Item type:Item, Summary Report: Drivers of in migration: Labour market participation of immigrants in their level of education(DataNB, 2026-10-15) Emery, Herb; McDonald, Ted; Afolabi, SophiaImmigration has become an increasingly important component of New Brunswick’s population growth, contributing to the province’s labour force and skill base. Understanding how immigrants’ education and experience align with their occupational outcomes post-migration is central to assessing the extent to which their human capital is recognized in the labour market. Using linked administrative and survey data from the Longitudinal Immigration Database (IMDB) and the 2016 Census, this study analyzes occupational skill levels, broad occupational categories, earnings patterns, and intended occupations at landing for immigrants who arrived in New Brunswick between 1980 and 2016. Comparisons with long term non immigrant residents provide insight into how educational background is associated with occupational outcomes across groups. The findings show that alignment between education and occupation is generally weaker for immigrants. Although immigrants often hold high levels of education, their non Canadian credentials and experiences are not consistently associated with the same occupational or earnings advantages observed among non immigrants. Although it improves among longer term residents, this may be due in part to survivor bias. Cohort differences, variation across occupational groups, and narrower returns to education highlight the complexity of immigrant labour market integration.Item type:Item, Drivers of in‑migration: Labour market participation of immigrants in their level of education(DataNB, 2026-10-16) Emery, Herbert; McDonald, Ted; Afolabi, SophiaImmigration has become an increasingly important component of New Brunswick’s population growth, contributing to the province’s labour force and skill base. Understanding how immigrants’ education and experience align with their occupational outcomes post-migration is central to assessing the extent to which their human capital is recognized in the labour market. Using linked administrative and survey data from the Longitudinal Immigration Database (IMDB) and the 2016 Census, this study analyzes occupational skill levels, broad occupational categories, earnings patterns, and intended occupations at landing for immigrants who arrived in New Brunswick between 1980 and 2016. Comparisons with long‑term non‑immigrant residents provide insight into how educational background is associated with occupational outcomes across groups. The findings show that alignment between education and occupation is generally weaker for immigrants. Although immigrants often hold high levels of education, their non‑Canadian credentials and experiences are not consistently associated with the same occupational or earnings advantages observed among non‑immigrants. Although it improves among longer‑term residents, this may be due in part to survivor bias. Cohort differences, variation across occupational groups, and narrower returns to education highlight the complexity of immigrant labour market integration.Item type:Item, Mineral prospectivity mapping in southwestern New Brunswick: Integration of geochemical, geophysical, and geological datasets in predictive modelling(University of New Brunswick, 2026-08) Ghane, Babak; Lentz, David R.Southwestern New Brunswick has undergone a prolonged and polyphase tectonic evolution that has generated a diverse spectrum of mineral systems. This research aimed to characterize the styles of mineralization and develop mineral prospectivity maps for gold and polymetallic mineralization across the study area. Extensive sampling of bedrock exposures and drill core was undertaken, with geochemical characterization performed using an Olympus Vanta™ portable X-ray fluorescence (pXRF) analyzer complemented by Instrumental Neutron Activation Analysis (INAA). Multi-element μ-XRF mapping of polished thick sections documented microscale elemental distributions and mineralization textures. Principal Component Analysis (PCA) combined with petrographic observations and alteration indices, delineates dominant propylitic-chloritic-sericitic± carbonatization assemblages. Multiple mineral systems were identified, including orogenic gold, intrusion-related gold, epithermal, and iron-oxide-copper-gold (IOCG) styles, commonly expressed through multi-phase quartz-carbonate veining and sulphidation. Airborne radiometric and apparent magnetic susceptibility datasets were calibrated using in situ petrophysical measurements including magnetic susceptibility, K (%), eTh (ppm), and eU (ppm), through an XGBoost Random Forest (XGBRF) regression workflow. The calibrated geophysical layers, integrated with additional predictor variables, were subsequently used as inputs to multiple ensemble-based classification algorithms to estimate the probability of gold mineralization across the region. Comparative model evaluation indicated that the XGBRF approach outperformed benchmark methods, including standalone Random Forest, XGBoost, and CatBoost. In addition, the prospectivity of intrusion-related Cu–Au (IRCG) mineralization was evaluated within a hybrid deep learning–machine learning framework. In this approach, a deep autoencoder network was first applied to the input feature space. These learned embeddings were subsequently used as inputs to downstream machine learning classifiers for mineral prospectivity prediction. Comparative modeling scenarios, including XGBoost and Random Forest applied to original features, latent embeddings, and integrated feature-embedding datasets, demonstrate that XGBoost trained on the combined dataset yields the highest predictive performance. The resulting prospectivity maps exhibit strong spatial correspondence with known mineral occurrences and delineate several high-priority exploration targets. Model robustness and uncertainty were assessed through confidence-weighted prospectivity maps that emphasize stable, high-confidence predictions. Collectively, the proposed frameworks provide a scalable and robust approach for regional-scale mineral exploration in southwestern New Brunswick and offers a transferable methodology for complex terranes worldwide.Item type:Item, Investigating inter-annual variation in cobble-bottom assemblages of fish and decapod crustaceans in the Beaver Harbour region of the Bay of Fundy over the last 17 years(University of New Brunswick, 2026-08) Brown, Maddison Jennetta; Rochette, Rémy; Hunt, HeatherGradual or sudden changes in biotic or abiotic factors can drive inter-annual variation in benthic communities. Despite their role as important nurseries, subtidal cobble ecosystems are understudied because of their interstitial nature, making monitoring difficult. This study quantifies changes in a community of fish and decapod crustaceans colonizing cobble-filled bio-collectors deployed from 2007-2024 at four sites in the Beaver Harbour region of the Bay of Fundy. Fish + large decapods at all sites, and fish + large decapods + small shrimp at both Beaver Harbour Lighthouse and Beaver Harbour East sites, showed significant seriation, progressively becoming dissimilar from the initial community. These two sites also showed two more pronounced sudden community shifts during this time. Community trends were mainly driven by changes in abundances of three fish (Cunner, Radiated Shanny, Shorthorn Sculpin) and two crab (Jonah, Atlantic Rock) species. These findings provide information on inter-annual changes of an understudied cobble bottom community potentially linked to condition changes in the Bay of Fundy.Item type:Item, Federated Class-Incremental Learning under class heterogeneity(University of New Brunswick, 2026-07) Hamedi, Parisa; Razavi-Far, RoozbehModern intelligent systems are increasingly deployed across distributed devices that operate on continuously evolving data, where neither centralised data collection nor static training assumptions are realistic. Federated Continual Learning (FCL), and specifically Federated Class-Incremental Learning (FCIL), combines the privacy-preserving collaboration of Federated Learning with the lifelong adaptability of Continual Learning. Yet FCL inherits and amplifies the difficulties of both fields, of which heterogeneity is the most pervasive: heterogeneous data and systems across clients, constrained communication, asynchronous task arrival, privacy leakage, and the stability–plasticity tension that drives catastrophic forgetting. This thesis frames the open challenges of FCIL through the lens of heterogeneity and advances the field on three fronts. First, it presents a structured survey that traces each FCL challenge back to its origin in Federated or Continual Learning and reviews methodologies along four families: replay-based, regularisation-based, parameter-isolation and decomposition, and knowledge-distillation methods. Second, it proposes FedLoCA, a low-rank class-adaptive framework for class-imbalance heterogeneity, where classes are unevenly distributed across clients. Client updates are decomposed into a globally shared low-rank basis, which aligns client geometries and reduces communication, and class-specific adaptation memories, which preserve dedicated knowledge pathways. Aggregation is performed class-by-class, with bounded shared-basis drift and class-wise normalisation limiting logit imbalance between recent and older classes. Third, it introduces FCIL-VC, the first framework, to our knowledge, studying task-cardinality heterogeneity in FCIL, where successive tasks introduce a variable number of new classes. It combines a non-parametric cosine prototype classifier, a permutation-invariant Cardinality-Agnostic Task Encoder (CATE) mapping tasks of arbitrary size to fixed-dimensional Gaussian embeddings, a closed-form 2-Wasserstein inter-task separator, and bounded exemplar replay with old-model knowledge distillation. Experiments on CIFAR-10, CIFAR-100 and TinyImageNet for FedLoCA, and on CIFAR-10, MNIST and Fashion-MNIST under variable cardinality schedules for FCIL-VC, show that the two methods consistently outperform representative FCIL baselines in final retained accuracy and average continual accuracy, and that the advantage widens precisely in the regimes where heterogeneity is most severe. Together, the survey, FedLoCA and FCIL-VC provide both a conceptual map and two concrete, communication-efficient pathways for scalable, privacy-aware lifelong learning under realistic federated heterogeneity.
