bluuebunny/crossref_metadata_2025_split huggingface dataset:
Wenn der Chatbot weiß, wo es lang geht – Ethische Fragen und mögliche Kriterien zum Einsatz von KI-gestützten Beratungssettings
Authors: Kieslinger Kristina , Nierobisch Kira 
Published: March 2024

ZusammenfassungDie aktuellen Diskussionen im Bildungssektor um ChatGPT verweisen auf die zunehmende Relevanz von generativer KI im Bereich der (Bildungs‑)Beratung. Obwohl avatargestützte Informationsberatungen bereits Realität sind und die Einsatzmöglichkeiten der KI-gestützten Techniken rege diskutiert werden, bleiben viele ethische Fragen unerwähnt. Ziel des vorliegenden Beitrages ist es, nach einer Klärung des Sachstandes die ethischen Fragen des Einsatzes von KI-gestützten Tools in (Bildungs‑)Beratungssettings zu thematisieren. Dafür wird ein pädagogisch verstandenes Beratungsverständnis bemüht, das auf Dimensionen von Reflexivität und Lernen verweist. Dieses wird mit dem Ansatz einer normativen Ethik aufgenommen, welche auf verbindliche Grundlagen der Profession verweist. Im Transfer werden dann mögliche Kriterien zum Einsatz von KI-gestützten Beratungssettings vorgestellt.
Delineating the significance of sharp boundaries between horizons and their synergetic linkage in calcareous vineyard soils
Authors: Jiménez-Ballesta Raimundo , Pérez-de-los-Reyes Caridad , Bravo Sandra , Gómez-Magan Andrés , Amorós Jose A. , García-Navarro Francisco J. 
Published: March 2024

AbstractThe present work was undertaken to appraise and delineate the significance of sharp boundaries between horizons in calcareous vineyard soils by evaluating parent material uniformity and pedogenesis. To do so, 10 soil profiles (selected from 275) were investigated. The profiles up to a depth of approximately 150 cm were opened. Macromorphological features were described following FAO guidelines. Physico-chemical properties were analysed by the usual methods. X-ray fluorescence (XRF) was used to determine elemental Ti and Zn concentrations. In terms of the main morphological characteristics, soils are reddish, and have a well-developed solum, a subangular or prismatic structure and friable moist consistency (B horizon) unlike the C horizon, which generally varies from friable to hard or very hard in a massive structure. These latter are usually related to the pedogenesis of evolved soils containing accumulations of calcium carbonate to form calcic or petrocalcic horizons. Other genetic horizons of the studied soils are argillic or cambic, which all lie directly on parent materials, in addition to a surface Ap anthropic horizon. Although the considered soils displayed a significant sharp boundary between some horizons, and even though some soil parent materials were stratified in nature and apparently formed of multidepositional regimes, certainly the vertical distribution of Ti/Zr displayed minor variations with no clear inflection with depth. Therefore, the parent materials of the investigated soils are apparently formed of homogeneous depositional materials. The morphological contrast within a profile is due mainly to pedological processes (they are very mature) rather than to geogenic ones. Nevertheless, the marked contrasts between horizons cannot be explained only by pedogenetic processes, but by other secondary processes related to agricultural use (mechanical impact of tillage). This means a partly artificial distinction that entails surface erosion and, consequently, a manifest redistribution fundamentally on surface horizons. Based on all this, it can be deduced that these soils are formed from local and not from extraregional materials.
Environmental and Innovative Fertilizer Development Strategies for Wheat Cultivation: Urea-Doped Hydroxyapatite, Biochar-Coated Diammonium Phosphate, and Biochar-Coated Urea in Basal and Top Dressing
Authors: Kardes Tugce Ayse , Gunes Aydin 
Published: March 2024

AbstractThe effectiveness of conventional fertilizers is quite low. This study aimed to develop alternative basal and top dressing fertilizers by comparing the effectiveness of urea-doped nanohydroxyapatite (UnHAP) and biochar-coated diammonium phosphate (BDAP), as well as biochar-coated Urea (BUrea) fertilizer, with conventional DAP and urea fertilizers in field conditions. In the first experiment, Urea or BUrea was used as a top dressing with UnHAP from the base. In the second experiment, Urea or BUrea was again used as a top dressing with BDAP from the base. In the experiments, a P rate of 80 kg P2O5 ha−1 was applied with DAP fertilizer as a reference. UnHAP and BDAP were applied at rates equal to or lower than the reference (60 and 40 kg P2O5 ha−1). All plots received 150 kg N ha−1 of Urea and BUrea, in comparison to DAP + Urea, the first year the highest grain production (4456 kg ha−1) with 80 kg ha−1 of DAP + BUrea, and in the second year, an equivalent amount of grain yield was obtained. In the second experiment, particularly in the first year, despite a decreased P rate up to 40 kg ha−1, yields were still comparable to the reference. Even at reduced doses of UnHAP and BDAP fertilizers compared to the reference, there was no significant decrease in the grain protein contents. High-efficiency UnHAP and BDAP have the potential to reduce fertilizer consumption. Conducting studies to determine the long-term effects of UnHAP under various ecological conditions would be advantageous.
Graphical Abstract
The Evolving Regulatory Paradigm of AI in MedTech: A Review of Perspectives and Where We Are Today
Authors: Zhou Karen , Gattinger Ginny 
Published: March 2024

AbstractArtificial intelligence (AI)-enabled technologies in the MedTech sector hold the promise to transform healthcare delivery by improving access, quality, and outcomes. As the regulatory contours of these technologies are being defined, there is a notable lack of literature on the key stakeholders such as the organizations and interest groups that have a significant input in shaping the regulatory framework. This article explores the perspectives and contributions of these stakeholders in shaping the regulatory paradigm of AI-enabled medical technologies. The formation of an AI regulatory framework requires the convergence of ethical, regulatory, technical, societal, and practical considerations. These multiple perspectives contribute to the various dimensions of an evolving regulatory paradigm. From the global governance guidelines set by the World Health Organization (WHO) to national regulations, the article sheds light not just on these multiple perspectives but also on their interconnectedness in shaping the regulatory landscape of AI.
The early Danian event (Dan-C2) and the latest Danian event (LDE): a case study from Gebel Kilabiya, Egypt
Authors: Orabi Orabi H. , Ismail Heba , Taha Saida 
Published: March 2024

AbstractAt the Gebel Kilabiya area, studies on foraminifera and isotopes were conducted during the Paleocene. The data revealed that the lowest part of the P2 Zone and the oldest peak, P1c, closely resemble the Dan-C2 event. Before Dan-C2, the diversity of the assemblages rapidly declined as the abundance of the agglutinant Spiroplectinella spectabilis, and calcareous benthic Bulimina spp., species commonly known as an opportunist, increased may be influenced by warmth and pH changes due to Deccan volcanism. The extinction of the planktic foraminifera genus Praemurica occurred shortly before the Latest Danian Event (LDE). Additionally, praemuricids were eventually supplanted by morozovellids in parallel. Warm morphogroups Praemurica, Morozovella, Igorina, and Acarinina are found in the low latitude group; they show a tendency towards warming in the late Paleocene (Zone P3a and P3b).
An empirical model for branding oriented by the environmental sustainability of the Amazon Rainforest: a hybrid structural equation modeling with fsQCA approach
Authors: Santos Luiz Diego Vidal , Holanda Francisco Sandro Rodrigues , Pedrotti Alceu , de Medeiros Raphael Mario Bastos , de Santana Oliveira Catuxe Varjão , Villwock Ana Paula Schervinski 
Published: March 2024

AbstractUtilizing the Theory of Planned Behavior, a meaningful connection between an organization and its consumers can be established, transcending mere appearances and delving into emotions, relationships, and brand experiences. In line with these principles, this study aims to present an empirical model grounded in factual evidence derived from the perspectives of entrepreneurs and consumers of environmentally sustainable brands in the Brazilian Amazon. The proposed model adopts a mixed-modeling approach, utilizing data analysis techniques such as fuzzy-set qualitative comparative analysis (fsQCA). Two independent data sets were collected: one from 8 companies operating in the Amazon rainforest and another from 535 potential consumers. By analyzing these datasets, consumer motivations for the consumption of sustainable brands, entrepreneurial practices, and the role of traditional communities and the forest environment in shaping brand perceptions were identified. The results reveal that, from the consumer’s perspective, the key factors influencing a positive inclination to purchase sustainable brands include the brand’s engagement with traditional com munities, perceived product quality, brand innovation, and a transparently communicated positioning strategy. These constructs form the core components of the main solution. This article provides valuable information for entrepreneurs operating sustainable brands in tropical forests as well as for policymakers in community-based entrepreneurship. It is also of interest to researchers and individuals involved in brand management as a strategy for empowering small businesses engaged in sustainable practices and community collaboration. The study’s findings and recommendations have the potential to foster more sustainable and socially responsible business practices, thereby enhancing local economies and contributing to environmental preservation.
Validation of farmers’ on-farm irrigation scheduling for optimal water utilization in Tigray Region, Ethiopia
Authors: Habtu Solomon 
Published: March 2024

AbstractUnwise use of irrigation water in water-scarce areas exacerbates water scarcity, diminishes crop yield, and leads to resource wastage. In the Tigray region of Ethiopia, where water is scarce and a determinant resource, no regional-level validation of on-farm irrigation scheduling had been conducted until now. The objectives of this study were to (i) validate farmers’ irrigation scheduling practices and (ii) optimize water consumption for increased irrigated area and number of farmers. Eight different irrigation schemes were purposively selected from the Tigray region. Primary data, acquired through field measurements, observations, and discussions, were supplemented with secondary data. Farmers' irrigation scheduling practices were predominantly of the "Fixed" type, characterized by constant irrigation intervals and amounts. Unfortunately, these practices were non-optimal, with 55% over-irrigation and 45% under-irrigation. Over-irrigation instances ranged from 1350 m3in a garlic plot within the Mesima scheme to a maximum of 1,327,067 m3in a maize plot within the Serenta scheme. The excess water could potentially be utilized to irrigate an additional area of 2 to 148 hectares, thereby benefiting 7 to 296 more farmers, respectively. Conversely, instances of under-irrigation in the Fre Lekatit scheme resulted in yield reductions of 10,445 kg for potatoes and 138,499 kg for maize. In the Tigray region, most of the schemes are semi-arid, emphasizing the imperative need for water conservation. Renovating regional-level irrigation scheduling by integrating performance assessment and enhancing water productivity at the field level, and establishing a remote-sensing-based 'Real-Time Irrigation Scheduling System' is deemed necessary to sustain dryland irrigated agriculture.
On the Opacity of Deep Neural Networks
Authors: Søgaard Anders 
Published: March 2024

AbstractDeep neural networks are said to be opaque, impeding the development of safe and trustworthy artificial intelligence, but where this opacity stems from is less clear. What are the sufficient properties for neural network opacity? Here, I discuss five common properties of deep neural networks and two different kinds of opacity. Which of these properties are sufficient for what type of opacity? I show how each kind of opacity stems from only one of these five properties, and then discuss to what extent the two kinds of opacity can be mitigated by explainability methods.
Open paleoenvironment and dry climate in south India immediately before the Youngest Toba Tuff eruption (~75 ka) are suggested by Vondrichnus structures at the Jwalapuram locality, Jurreru valley
Authors: Singh Ajab 
Published: March 2024

AbstractThe Younger Toba Tuff (YTT) eruption is regarded as one of the largest of its time and possibly responsible for changing past climate and vegetation from C3 to C4 in the Indian subcontinent. A paleosol constituting a Toba pre-tephra horizon at the Jwalapuram locality, exhibits the preservation of biogenic structures identified as Vondrichnus planoglobus and Vondrichnus obovatus. This study investigated their paleoecological and paleoenvironmental significance. These structures are hard and compact, rounded to sub-rounded, spherical to sub-spherical bodies with empty chambers, surrounded by carbonate layers, and preserved in close proximity to termite pipes and nests and rhizolith structures. Their occurrence in the Jwalapuram area is significant, as the locality has been well documented as suitable for reconstruction of past climate and vegetation in light of the impact of the YTT eruption. Based on the present findings, we assume that the investigated locality would likely have an insect population and bush to scrub vegetation, indicating a dry environment immediately before the YTT eruption.
ASSESSING 14C BLANKS IN THE SMALL-SCALE ANALYSIS OF N-ALKANE COMPOUND-SPECIFIC-RADIOCARBON-ANALYSIS
Authors: Reetz Kristina , Friedrich Ronny , Birk Jago J , Rosendahl Wilfried , Fiedler Sabine 
Published: March 2024

ABSTRACTCompound-specific radiocarbon analysis (CSRA) provides the possibility to date sample material at a molecular level. N-alkanes are considered as specific compounds with high potential to CSRA. As these compounds originate from plant waxes, their radiocarbon (14C) analysis can provide valuable information about the age and origin of organic materials. This helps to reconstruct and understand environmental conditions and changes in vegetation in the past. However, CSRA has two main challenges: The small sample size of CSRA samples, making them extremely sensitive to blank effects, and the input of unknown amounts of extraneous carbon during the analytical procedure. According to the previous study from Sun and co-workers, we used different-sized aliquots of leaves Fagus sylvatica (nC27, nC29) and Festuca rubra agg (nC31, nC33) as modern standards and two commercial standards (nC26, nC28) as fossil standards for blank determination. A third commercial standard (nC27) with predetermined radiocarbon content of F14C = 0.71 (14C age of 2700 BP) serves to evaluate the blank correction. We found that the blank assessment of Sun and co-workers is also applicable to n-alkanes, with a minimum sample size of 15 µg C for dependable CSRA dates. We determined that the blank introduced during the analytical procedure has a mass of (4.1 ± 0.7) µg carrying a radiocarbon content of F14C = 0.25 ± 0.05. Applying the blank correction to a sediment sample from Lake Holzmaar (Germany) shows that all four isolated n-alkanes have similar 14C ages. However, the bulk material of the sediment and branches found in the sediment core are younger than the CSRA dates. We conclude that the disparity between the actual age of analysed organic material and the age inferred from radiocarbon results, which can occur in sediment traps due to delayed deposition, is the reason for the CSRA age.
Determinants of confrontation naming deficits on the Boston Naming Test associated with transactive response DNA-binding protein 43 pathology
Authors: Robinson Carling G. , Goodrich Austin W. , Weigand Stephen D. , Pham Nha Trang Thu , Carlos Arenn F. , Buciuc Marina , Murray Melissa E. , Nguyen Aivi T. , Reichard R. Ross , Knopman David S. , Petersen Ronald C. , Dickson Dennis W. , Utianski Rene L. , Whitwell Jennifer L. , Josephs Keith A. , Machulda Mary M. 
Published: March 2024

AbstractObjective:To determine whether poorer performance on the Boston Naming Test (BNT) in individuals with transactive response DNA-binding protein 43 pathology (TDP-43+) is due to greater loss of word knowledge compared to retrieval-based deficits.Methods:Retrospective clinical-pathologic study of 282 participants with Alzheimer’s disease neuropathologic changes (ADNC) and known TDP-43 status. We evaluated item-level performance on the 60-item BNT for first and last available assessment. We fit cross-sectional negative binomial count models that assessed total number of incorrect items, number correct of responses with phonemic cue (reflecting retrieval difficulties), and number of “I don’t know” (IDK) responses (suggestive of loss of word knowledge) at both assessments. Models included TDP-43 status and adjusted for sex, age, education, years from test to death, and ADNC severity. Models that evaluated the last assessment adjusted for number of prior BNT exposures.Results:43% were TDP-43+. The TDP-43+ group had worse performance on BNT total score at first (p = .01) and last assessments (p = .01). At first assessment, TDP-43+ individuals had an estimated 29% (CI: 7%–56%) higher mean number of incorrect items after adjusting for covariates, and a 51% (CI: 15%–98%) higher number of IDK responses compared to TDP-43−. At last assessment, compared to TDP-43−, the TDP-43+ group on average missed 31% (CI: 6%–62%; p = .01) more items and had 33% more IDK responses (CI: 1% fewer to 78% more; p = .06).Conclusions:An important component of poorer performance on the BNT in participants who are TDP-43+ is having loss of word knowledge versus retrieval difficulties.
Identifying Climate Impacts From Different Stratospheric Aerosol Injection Strategies in UKESM1
Authors: Wells Alice F. , Henry Matthew , Bednarz Ewa M. , MacMartin Douglas G. , Jones Andy , Dalvi Mohit , Haywood James M. 
Published: March 2024

AbstractStratospheric Aerosol Injection (SAI) is a proposed method of climate intervention aiming to reduce the impacts of human‐induced global warming by reflecting a portion of incoming solar radiation. Many studies have demonstrated that SAI would successfully reduce global‐mean surface air temperatures; however the vast array of model scenarios and strategies result in a diverse range of climate impacts. Here we compare two SAI strategies—a quasi‐ equatorial injection and a multi‐latitude off‐equatorial injection—simulated with the UK Earth System Model (UKESM1), both aiming to reduce the global‐mean surface temperature from that of a high‐end emissions scenario to that of a moderate emissions scenario. We compare changes in the surface and stratospheric climate under each strategy to determine how the climate response depends on the injection location. In agreement with previous studies, an equatorial injection results in a tropospheric overcooling in the tropics and a residual warming in the polar regions, with substantial changes to stratospheric temperatures, water vapor and circulation. Previous comparisons of equatorial versus off‐equatorial injection strategies are limited to two studies using different versions of the Community Earth System Model. Our study evaluates how the climate responds in UKESM1 under these injection strategies. Our results are broadly consistent with previous findings, concluding that an off‐equatorial injection strategy can minimize regional surface temperature and precipitation changes relative to the target. We also present more in‐depth analysis of the associated changes in Hadley Circulation and regional temperature changes, and call for a new series of inter‐model SAI comparisons using an off‐equatorial strategy.
Our Vision for JGR: Machine Learning and Computation
Authors: Camporeale E. , Marino R. ,   the Editorial Board
Published: March 2024

AbstractThis editorial introduces the inaugural issue of the Journal of Geophysical Research: Machine Learning and Computation to the scientific community, elucidating the motivations and vision behind its establishment. The landscape of computational tools for geoscientists has undergone a rapid transformation in the last decade, akin to a new scientific revolution challenging the traditional scientific method. The paradigm shift emphasizes the integration of data‐driven methods and the possibility of predicting and/or reproducing the evolution of natural phenomena with computers as the fourth pillar of scientific discovery, sparking debates on trustworthiness, and ethical implications. The data science revolution is fueled by the convergence of advancements, including the big‐data revolution, GPU market expansion, and significant investments in Artificial Intelligence and high performance computing by both institutional and private players. This transformation has given rise to a trans‐disciplinary community that has investigated a wide range of questions under the lens of machine learning (ML) approaches and has generally advanced the field of computational methods within the broader geosciences community, the core of the American Geophysical Union (AGU) membership. Responding to an unmet demand in the existing worldwide editorial offer, the Journal of Geophysical Research: Machine Learning and Computation aims to serve as an intellectual crucible, fostering collaborations across multiple geophysical disciplines and data scientists. The journal welcomes papers with strong methodological developments that allow for geoscience advancements grounded in specific computational and data‐driven methods, leveraging ML as well as innovative computational strategies, and leading to breakthrough discoveries and original scientific outcomes. Authors are encouraged to balance succinctness in introducing methods with a thorough exploration of the novelty of the work proposed and its future applications placing special emphasis on the connection between the data science approach and the scientific outcome, considering a broad readership. Emphasis on result reproducibility aligns with AGU guidance, inviting active participation from the community in shaping geophysical research in the era of machine learning and computation.