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While critical for the practical progress of spectrum sharing, modulation recognition has so far been investigated under unrealistic assumptions: (i) a transmitter's bandwidth must be scanned alone and in full, (ii) prior knowledge of the technology must be available and (iii) a transmitter must be trustworthy. In reality these assumptions cannot be readily met, as a transmitter's bandwidth may only be scanned intermittently, partially, or alongside other transmitters, and modulation obfuscation may be introduced by short-lived scans or malicious activity. This paper presents LinksIQ, which bridges the gap between real-world spectrum sensing and the growing body of modrec methods designed under simplifying assumptions. Our key insight is that ordered IQ samples form distinctive patterns across modulations, which persist even with scan deficiencies. We mine these patterns through a Fisher Kernel framework and employ lightweight linear support vector machine for modulation classification. LinksIQ is robust to noise, scan partiality and data biases without utilizing prior knowledge of transmitter technology. Its accuracy consistently outperforms baselines in both simulated and real traces. We evaluate LinksIQ performance in a testbed using two popular SDR platforms, RTL-SDR and USRP. We demonstrate high detection accuracy (i.e. 0.74) even with a $20 RTL-SDR scanning at 50% transmitter overlap. This constitutes an average of 43% improvement over existing counterparts employed on RTL-SDR scans. We also explore the effects of platform-aware classifier training and discuss implications on real-world modrec system design. Our results demonstrate the feasibility of low-cost transmitter fingerprinting at scale.