ReviewiScience2026
Beyond predictive accuracy: A case for mechanism-informed, uncertainty-aware machine learning in food microbiology.
Review in iScience, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Foodborne illness and avoidable food waste both depend on decisions about how microorganisms behave in a food. Machine-learning (ML) models can achieve strong predictive accuracy within a defined data domain, but a held-out test error alone does not establish reliability for food-safety decisions. Conventional predictive-microbiology models are usually empirical rather than fully mechanistic, although their parameters have biological interpretation; ML offers flexible learning of complex, high-dimensional relationships. Mechanism-informed hybrids can incorporate kinetic or physiological constraints within a learning workflow. Such constraints may improve plausibility, data efficiency, and interpretability within a declared applicability domain, but they do not confer universal or unrestricted extrapolation. We outline four hybrid design patterns and propose a food-specific validation framework that includes context-relevant external validation, calibrated predictive uncertainty, and decision-focused evaluation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.