Page 5 likely to yield the most reliable results. Kirchoff et al. (2011) provide a more recent comparison with three researchers (two expert, one novice without crater counting experience) from the same lab who used the same technique to identify, measure, and, in this case, classify craters by preservation state. They used Lunar Reconnaissance Orbiter Camera Wide-Angle Camera (LROC WAC) images of Mare Orientale. The two experienced analysts had counts that differed by 20-40% in a given diameter range, while the novice counter identified numerous features that are probably not craters, differing from the other two by >100% over some diameter ranges. They also had significant variation among the preservation states attributed to each crater, despite a relatively coarse fourpoint scale. This work showed that despite common thinking that crater counting is fairly easy and straightforward, there is a learning curve and an individual's crater counts should be discarded during the learning process. It also showed that even well defined crater morphologies may be difficult to classify uniformly. Hiesinger et al. (2012) also focused on lunar craters, in their case using LROC Narrow-Angle Camera (NAC) images at approximately 0.5 m/px. They were interested in reproducible results for better understanding the lunar cratering flux and performed a single test with two experienced researchers who used the same technique on the same image. The Heisinger et al.(2012) team found an overall variation of only ±2% between their analysts, a dispersion significantly less than previous work.What this brief review indicates is that while there has been some discussion in the literature about agreement between different researchers' crater identifications, (a) there has been no thorough discussion on researcher variability, (b) no published study discusses the variability when using different techniques for crater identification and measurement, (c) variation in crater morphology has not been discussed (e.g., sub-km craters appear substantially different at NAC pixel scales when compared with multi-km craters at WAC pixels scales), and (d) expert results have not been extensively compared with how well untrained or minimally trained crater counters do with the identification and measurement process. Given the proliferation of internet

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Schrödinger basin is a well-preserved peak-ring basin located on the lunar farside, along the rim of the much larger South Pole – Aitken (SPA) basin. The relatively young age (Lower Imbrian series, or 3.8 Ga) of this basin makes it an ideal site to study the geology of peak-ring basins in general, and the geological history of SPA specifically. Impact materials still recognizable include a well-defined crater rim, wall terraces, quasi-circular peak ring, and interior and exterior melt units. A small pyroclastic deposit fills a portion of the basin floor, along with several mare patches. This study uses Clementine multispectral ultraviolet–visible (UV–VIS) data, and a limited set of higher spectral resolution Chandrayaan-1 Moon Mineralogy Mapper (M3) data, as well as radar, camera, and topography data from the Lunar Reconnaissance Orbiter to better understand Schrödinger’s geology. Sampled spectral profiles and linear unmixing models applied to the Clementine data indicate there is a heterogeneous distribution of both anorthositic and basaltic materials in the crater floor. M3 data further validates this observation, and the high spectral resolution shows that most of the mafic content is dominated by pyroxene. These results challenge the traditional assumption that Schrödinger was formed in mostly highland terrain. Our assessment brings forth a new understanding regarding the placement of Schrödinger within SPA and the role SPA impact materials played in shaping the composition of Schrödinger.

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