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Gradable adjectives denote properties that are relativized to contextual thresholds of application: how long an object must be in order to count as long in a context of utterance depends on what the threshold is in that context. But thresholds are variable across contexts and adjectives, and are in general uncertain. This leads to two questions about the meanings of gradable adjectives in particular contexts of utterance: what truth conditions are they understood to introduce, and what information are they taken to communicate? In this paper, we consider two kinds of answers to these questions, one from semantic theory, and one from Bayesian pragmatics, and assess them relative to human judgments about truth and communicated information. Our findings indicate that Bayesian accounts can model human judgments about what is communicated better than they can model human judgments about truth conditions, and their weakness on the latter is mainly because they fall short in accurately predicting the relevant threshold distributions. We explore the possibility that the overall performance of the Bayesian accounts can be potentially improved when they are supplemented with the threshold conventions postulated by semantic theory.