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Accurate knowledge of the ice thickness distribution and glacier bed topography is essential for predicting dynamic glacier changes and the future developments of downstream hydrology, which are impacting the energy sector, tourism industry and natural hazard management. Using AIR-ETH, a new helicopter-borne ground-penetrating radar (GPR) platform, we measured the ice thickness of all large and most medium-sized glaciers in the Swiss Alps during the years 2016–20. Most of these had either never or only partially been surveyed before. With this new dataset, 251 glaciers – making up 81% of the glacierized area – are now covered by GPR surveys. For obtaining a comprehensive estimate of the overall glacier ice volume, ice thickness distribution and glacier bed topography, we combined this large amount of data with two independent modeling algorithms. This resulted in new maps of the glacier bed topography with unprecedented accuracy. The total glacier volume in the Swiss Alps was determined to be 58.7 ± 2.5 km3 in the year 2016. By projecting these results based on mass-balance data, we estimated a total ice volume of 52.9 ± 2.7 km3 for the year 2020. Data and modeling results are accessible in the form of the SwissGlacierThickness-R2020 data package.

Traditionally, helicopter-borne ground-penetrating radar (GPR) systems are operated with a single pair of bistatic dipole antennas to measure the thickness of glaciers. We demonstrate numerically that the directivity of the radiation pattern of single airborne dipoles do not correspond to an ideal full-space solution if the antennas are employed at typical flight heights. These directionality effects can degrade the quality of the subsurface images significantly, when the GPR antennas are orientated unfavorably. Since an adjustment of the antenna orientation is impractical during flight, we have developed a novel dual-polarization helicopter-borne GPR system consisting of two orthogonal pairs of commercial antennas in broadside configuration. To overcome the image quality deficits of the individual channels, we apply a pseudo-scalar approach in which we combine the data of both polarizations. Results of helicopter-borne GPR surveys on two alpine glaciers in Switzerland reveal more coherent bedrock reflections in the summed data compared with single dipole pair profiles. Generally, the dual-polarization setup is more suitable than a single antenna systems, because it is more versatile and less prone to directional effects caused by the placement of the dipole antennas in relation to undulating subsurface reflectors.

This special issue features the selected works of authors who have presented papers at the 2022 iteration of the Joint Conference on Digital Libraries (JCDL) in Cologne, Germany. The motto of the conference was “Bridging Worlds” and was run as a fully hybrid event. Ten papers covering all aspects of Digital Libraries, namely Natural Language Processing, Information Retrieval, User Behavior, Scholarly Communication, Classification, Information Extraction are included in this issue.

»Zur Anwendung von Informetrie für Digitale Bibliotheken. Ein Überblick über Grundlagen, Probleme und aktuelle Zugänge«. The foundation of every research project is a comprehensive literature review. The search for scientific literature in information systems is a discipline at the intersection of information retrieval and digital libraries; recent user studies in both fields show two typical weaknesses of the classical IR approach: ranking of retrieved and maybe relevant documents and the language problem during the query formulation phase. At the same time the performance of traditional retrieval systems that rely primarily on textual document and query features have been stagnating for years, as could be observed in IR evaluation campaigns such as TREC or CLEF. Therefore alternative approaches to surpass these two problem fields are needed. Recent developments in the area of applied informetrics show very promising effects by using long-known informetric and bibliometric methods like the analysis of power-law distributions described by Lotka's, Zipf's or Bradford's laws, or the application of co-occurrences analysis for entities like authors, journals or references of scientific literature. This work will concentrate on the description of the open problems and the current approaches to surpass these by using applied informetrics methodologies.

Purpose Survey-based studies suggest that search engines are trusted more than social media or even traditional news, although cases of false information or defamation are known. The purpose of this paper is to analyze query suggestion features of three search engines to see if these features introduce some bias into the query and search process that might compromise this trust. The authors test the approach on person-related search suggestions by querying the names of politicians from the German Bundestag before the German federal election of 2017. Design/methodology/approach This study introduces a framework to systematically examine and automatically analyze the varieties in different query suggestions for person names offered by major search engines. To test the framework, the authors collected data from the Google, Bing and DuckDuckGo query suggestion APIs over a period of four months for 629 different names of German politicians. The suggestions were clustered and statistically analyzed with regards to different biases, like gender, party or age and with regards to the stability of the suggestions over time. Findings By using the framework, the authors located three semantic clusters within the data set: suggestions related to politics and economics, location information and personal and other miscellaneous topics. Among other effects, the results of the analysis show a small bias in the form that male politicians receive slightly fewer suggestions on “personal and misc” topics. The stability analysis of the suggested terms over time shows that some suggestions are prevalent most of the time, while other suggestions fluctuate more often. Originality/value This study proposes a novel framework to automatically identify biases in web search engine query suggestions for person-related searches. Applying this framework on a set of person-related query suggestions shows first insights into the influence search engines can have on the query process of users that seek out information on politicians.

The search for scientific literature in scientific information systems is a discipline at the intersection between information retrieval and digital libraries. Recent user studies show two typical weaknesses of the classical IR model: ranking of retrieved and maybe relevant documents and the language problem during the query formulation phase. At the same time traditional retrieval systems that rely primarily on textual document and query features are stagnating for years, as it could be observed in IR evaluation campaigns such as TREC or CLEF. Therefore alternative approaches to surpass these two problem fields are needed. Recent developments in the area of applied informetrics show very promising effects by using long-known informetric and bibliometric methods like the analysis of power-law distributions described by Lotka's, Zipf's or Bradford's laws. This contribution will concentrate on the description of the different approaches in digital libraries, information retrieval, and informetrics to give a broad overview on current methods in applied informetrics. This article contains: 1. Introduction 2. Digital Libraries 3. User Estimation of Relevance and Computer-Generated Ranking 4. Evaluation of Information Retrieval Systems 5. Informetrics 6. Discussion. Adapted from the source document.

»Information Retrieval and Informetrics: The Application of Informetric Methods in Digital Libraries«. The search for scientific literature in scientific information systems is a discipline at the intersection between information retrieval and digital libraries. Recent user studies show two typical weaknesses of the classical IR model: ranking of retrieved and maybe relevant documents and the language problem during the query formulation phase. At the same time traditional retrieval systems that rely primarily on textual document and query features are stagnating for years, as it could be observed in IR evaluation campaigns such as TREC or CLEF. Therefore alternative approaches to surpass these two problem fields are needed. Recent developments in the area of applied informetrics show very promising effects by using long-known informetric and bibliometric methods like the analysis of power-law distributions described by Lotka's, Zipf's or Bradford's laws. This contribution will concentrate on the description of the different approaches in digital libraries, information retrieval, and informetrics to give a broad overview on current methods in applied informetrics.This article contains: 1. Introduction 2. Digital Libraries 3. User Estimation of Relevance and Computer-Generated Ranking 4. Evaluation of Information Retrieval Systems 5. Informetrics 6. Discussion

The foundation of every research project is a comprehensive literature review. The search for scientific literature in information systems is a discipline at the intersection of information retrieval and digital libraries; recent user studies in both fields show two typical weaknesses of the classical IR approach: ranking of retrieved and maybe relevant documents and the language problem during the query formulation phase. At the same time the performance of traditional retrieval systems that rely primarily on textual document and query features have been stagnating for years, as could be observed in IR evaluation campaigns such as TREC or CLEF. Therefore alternative approaches to surpass these two problem fields are needed. Recent developments in the area of applied informetrics show very promising effects by using long-known informetric and bibliometric methods like the analysis of power-law distributions described by Lotka's, Zipfs or Bradford's laws, or the application of co-occurrences analysis for entities like authors, journals or references of scientific literature. This work will concentrate on the description of the open problems and the current approaches to surpass these by using applied informetrics methodologies. Adapted from the source document.

Information retrieval systems have been evaluated using the Cranfield paradigm for many years. This paradigm allows a systematic, fair, and reproducible evaluation of different retrieval methods in fixed experimental environments. However, real-world retrieval systems must cope with dynamic environments and temporal changes that affect the document collection, topical trends, and the individual user's perception of what is considered relevant. Yet, the temporal dimension in IR evaluations is still understudied. To this end, this work investigates how the temporal generalizability of effectiveness evaluations can be assessed. As a conceptual model, we generalize Cranfield-type experiments to the temporal context by classifying the change in the essential components according to the create, update, and delete operations of persistent storage known from CRUD. From the different types of change different evaluation scenarios are derived and it is outlined what they imply. Based on these scenarios, renowned state-of-the-art retrieval systems are tested and it is investigated how the retrieval effectiveness changes on different levels of granularity. We show that the proposed measures can be well adapted to describe the changes in the retrieval results. The experiments conducted confirm that the retrieval effectiveness strongly depends on the evaluation scenario investigated. We find that not only the average retrieval performance of single systems but also the relative system performance are strongly affected by the components that change and to what extent these components changed.