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Seren Smith

As seen in: MDPI
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Articles by Seren Smith

Spatial-Aware Uncertainty Quantification for Low-Cost PM₂.₅ Sensors: GeoConformal Prediction vs. Monte Carlo Dropout Under Wildfire Distribution Shift

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By Anusha Srirenganathan Malarvizhi, Kaylee Smith, Seren Smith
| Preprints.org
You are already at the latest version Low-cost air quality sensors can expand PM₂.₅ monitoring networks but require calibration against regulatory-grade monitors to correct systematic bias. Although interest in predictive uncertainty for air quality estimation has increased, uncertainty reliability under spatial sparsity and wildfire-induced distribution shift remains poorly understood.
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A Systematic Study of Popular Software Packages and AI/ML Models for Calibrating In Situ Air Quality Data: An Example with Purple Air Sensors

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By Seren Smith, Theodore Trefonides, Anusha Srirenganathan Malarvizhi, Shyra Lagarde
| MDPI
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A Systematic Study of Popular Software Packages and AI/ML Models for Calibrating in-situ Air Quality Data: An example with Purple Air sensors

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By Seren Smith, Theodore Trefonides, Anusha Srirenganathan Malarvizhi, Shyra Lagarde
| Preprints.org
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Scenario-based learning

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By Seren Smith
| ucl.ac.uk
Seren Smith, M; Warnes, S; Vanhoestenberghe, A; (2018) Scenario-based learning. In: Davies, JP and Pachler, N, (eds.) Teaching and Learning in Higher Education: Perspectives from UCL. (pp. 144-156). UCL IOE Press: London, UK. Continuing the theme of using the real world as a teaching resource, Smith, Warnes and van Hoestenberghe describe learning scenarios where students find their own way and make their own choices in exploring an authentic situation.
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