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  1. Understanding local hydraulic conditions is imperative to coastal harmful algal bloom (HAB) monitoring. The research summarized herein describes how the locations and tidal phases selected for coastal hazard sampling can influence measurement results used to guide management decisions for HABs. Our study was conducted in Frenchman Bay, Maine, known for its complex deglaciated coastline, strong tidal influence, and shellfishing activities that are susceptible to problematic HABs such as those produced by some species (spp.) of the diatom genus Pseudo-nitzschia. In-situ measurements of current velocity, density, and turbulence collected over a semidiurnal tidal cycle and a companion numerical model simulation of the study area provide concurrent evidence of two adjacent counter-rotating sub-mesoscale eddies (2–4 km diameter) that persist in the depth-averaged residual circulation. The eddies are generated in the wake of several islands in an area with abrupt bathymetric gradients, both legacy conditions partly derived from deglaciation ∼15 kya. Increased concentrations of Pseudo-nitzschia spp. measured during the semidiurnal survey follow a trend of elevated turbulent dissipation rates near the water surface, indicating that surface sampling alone might not adequately indicate species abundance. Additional measurements of Pseudo-nitzschia spp. from two years of weekly sampling in the region show that algal cell abundance is highest where residual eddies form. These findings provide incentive to examine current practices of HAB monitoring and management by linking coastal geomorphology to hydraulic conditions influencing HAB sampling outcomes, coastal morphometric features to material accumulation hotspots, and millennial time scales to modern hydraulic conditions. 
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    Free, publicly-accessible full text available October 20, 2025
  2. Darran Yates (Ed.)
  3. Abstract

    This paper examines constraints and their role in scientific explanation. Common views in the philosophical literature suggest that constraints are non-causal and that they provide non-causal explanations. While much of this work focuses on examples from physics, this paper explores constraints from other fields, including neuroscience, physiology, and the social sciences. I argue that these cases involve constraints that are causal and that provide a unique type of causal explanation. This paper clarifies what it means for a factor to be a constraint, when such constraints are causal, and how they figure in scientific explanation.

     
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  4. This paper provides an analysis of explanatory constraints and their role in scientific explanation. This analysis clarifies main characteristics of explanatory constraints, ways in which they differ from “standard” explanatory factors, and the unique roles they play in scientific explanation. While current philosophical work appreciates two main types of explanatory constraints, this paper suggests a new taxonomy: law-based constraints, mathematical constraints, and causal constraints. This classification helps capture unique features of constraint types, the different roles they play in explanation, and it includes causal constraints, which are often overlooked in this literature.

     
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  5. We explore Madole & Harden’s (2022) suggestion that single-nucleotide polymorphism (SNP)/trait correlations are analogous to randomized experiments and thus can be given a causal interpretation. 
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  6. According to mainstream philosophical views causal explanation in biology and neuroscience is mechanistic. As the term ‘mechanism’ gets regular use in these fields it is unsurprising that philosophers considerit important to scientific explanation. What is surprisingis that they consider it the only causal term of importance. This paper provides an analysis of a new causal concept—it examines the cascade concept in science and the causal structure it refers to. I argue that this conceptis importantly different from the notion of mechanism and that this difference matters for our understanding of causation and explanation in science. 
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  7. Edward N. Zalta & Uri Nodelman (Ed.)
    This entry discusses some accounts of causal explanation developed after approximately 1990. Our focus in this entry is on the following three accounts – Section 1 those that focus on mechanisms and mechanistic explanations, Section 2 the kairetic account of explanation, and Section 3 interventionist accounts of causal explanation. All of these have as their target explanations of why or perhaps how some phenomenon occurs (in contrast to, say, explanations of what something is, which is generally taken to be non-causal) and they attempt to capture causal explanations that aim at such explananda. Section 4 then takes up some recent proposals having to do with how causal explanations may differ in explanatory depth or goodness. Section 5 discusses some issues having to do with what is distinctive about causal (as opposed to non-causal) explanations. 
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  8. Potochnik, Angela (Ed.)
    This paper explores a distinction among causal relationships that has yet to receive attention in the philosophical literature, namely, whether causal relationships are reversible or irreversible. We provide an analysis of this distinction and show how it has important implications for causal inference and modeling. This work also clarifies how various familiar puzzles involving preemption and over-determination play out differently depending on whether the causation involved is reversible. 
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  9. Abstract

    Social scientists appeal to various “structures” in their explanations including public policies, economic systems, and social hierarchies. Significant debate surrounds the explanatory relevance of these factors for various outcomes such as health, behavioral, and economic patterns. This paper provides a causal account of social structural explanation that is motivated by Haslanger (2016). This account suggests that social structure can be explanatory in virtue of operating as a causal constraint, which is a causal factor with unique characteristics. A novel causal framework is provided for understanding these explanations–this framework addresses puzzles regarding the mysterious causal influence of social structure, how to understand its relation to individual choice, and what makes it the main explanatory (and causally responsible) factor for various outcomes.

     
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