A data-driven approach in the search for Antarctic meteorites

Abstract

<p>Meteorites provide an unparalleled view on the origin and evolution of the solar system. Antarctica is the most productive region for collecting meteorites, as the visually contrasting meteorites are easily detectable and tend to concentrate at specific areas exposing blue ice. Blue ice areas act as meteorite stranding zones if the flow of the ice sheet and specific geographical and climatological settings combine favorably. Previously, possible meteorite stranding zones were identified by chance or through visual examination of remote sensing data, which limits the discovery of new locations for future meteorite searching campaigns.</p><p>In this study, various state-of-the-art datasets are combined in a machine learning approach to estimate the likeliness of a blue ice area to be a meteorite stranding zone. Input data for a generative classifier consists of ca. 13,000 reprojected meteorite finding locations (positive observations) and 2,000,000 unlabeled observations, for which the presence of meteorites is unknown. Four features have been selected, representing the typical conditions in which meteorites are found: exposure of blue ice (radar backscatter), cold surface conditions and negative surface mass balance (surface temperature and surface slope), and almost stagnant ice flow (surface velocities). With these features, the probability of the presence of meteorites is computed for each unlabeled observation at blue ice areas. These probabilities are computed by evaluating the multidimensional density distributions of the observations on the unlabeled observations and combining these with the prior probabilities of the two classes (positive and unlabeled). As the set of training data does contain only positive and unlabeled observations, the prior probabilities are scaled. The amount of scaling is decided by maximizing the harmonic mean between precision and sensitivity, which are estimated in a cross-validation using negative observations of sites known to be absent of meteorites. In the post-processing, the pixels that likely contain meteorites are clustered, resulting in several hundreds of meteorite stranding zones.</p><p>Results show that the first continent-wide meteorite stranding zone classification is ca. 70-80% accurate (first estimate, based on independent test data). The post-processed results reveal the existence of major unexplored meteorite stranding zones, some of which are in close proximity to existing research stations. The quest to collect the meteorites remaining at the surface of the ice sheet, the number of which is estimated to exceed those already collected to date, will greatly benefit from our newly provided meteorite map.</p>

Overview of the Lost Meteorites of Antarctica field campaigns

“…Given a realistic "searchable" ice area of 11.25 km 2 (assessed on the 2018-2019 field team's visual inspection of the site and a review of satellite imagery of ice as being crevasse free), the predicted surface number of meteorite finds on this icefield (Outer Recovery west): was in the range 128 AE 33 (where errors represent min and max estimates). Note that this calculation was independent of different meteorite density maps later published by Tollenaar et al (2022). Using the Evatt et al (2016) noniron:iron-rich meteorite Antarctic find ratio we predicted that potentially the missing, englacially trapped, iron meteorites would number 5 for this area (i.e., 0.5 buried meteorite per 1 km 2 of ice).…”

The Ethics of Publishing Biomedical and Natural Products Research

“…Almost concurrently, scientists demonstrated how AI can be a powerful scientific tool by developing an AI to find meteorites in Antarctica. 21 ■ CASE 3: DNA BARCODING OF HERBAL PRODUCTS Over the decades, a plethora of research on the science of plant-derived human health supplements has led to several widely accepted "gold standards" that guide ethical research and good industrial practices. These include the following: (a) botanical raw materials require well-documented sourcing and authentication; (b) production of botanical extracts requires rationales for the chosen methodology; (c) botanical materials including extracts represent mixtures of high chemical complexity (metabolomes); (d) characterization data for (a)−(c) are prerequisites for any biological and clinical evaluation, which in turn must employ in vitro and in vivo assays as well as clinical protocols that are adequate for the intended biological/clinical end points to be addressed.…”