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International Ocean Discovery Program Site U1534 presents an opportunity to study the evolution of subantarctic surface waters in the Atlantic Ocean and investigate Subantarctic Front dynamics during glacial–interglacial transitions. Here we document stable carbon and oxygen isotopes (δ13C and δ18O) in the planktonic foraminifera Globigerina bulloides and Neogloboquadrina pachyderma (sinistral), as well as species abundances of all planktonic foraminifera present in the splice section of Site U1534 interpreted to represent Marine Isotope Stage 11 based on the shipboard stratigraphy. The planktonic assemblage is dominated by N. pachyderma (sinistral), with minor occurrences of other calcareous species; the lowest N. pachyderma (sinistral) abundances suggest the warmest upper ocean temperatures at ~43–44 m core composite depth below seafloor, Method A (CCSF-A). Results demonstrate that δ13C in N. pachyderma (sinistral) and G. bulloides varies from −0.12‰ to 1.01‰ and from 0.19‰ to 1.11‰, respectively. δ18O in N. pachyderma (sinistral) and G. bulloides varies from 1.68‰ to 2.92‰ and from 2.14‰ to 2.97‰, respectively. δ18O values oscillate through the section but are generally lowest near 43–44 m CCSF-A. 

Characterization of fungal spider pathogens lags far behind their insect counterparts. In addition, little to nothing is known concerning the ecological reservoir and/or fungal entomopathogen community surrounding infection sites. Five infected spider cadavers were identified in the neo-tropical climate of north-central Florida, USA, from three of which viable cultures were obtained. Multi-locus molecular phylogenetic and morphological characterization identified one isolate as a new Gibellula species, here named, Gibellula floridensis, and the other isolates highly similar to Parengyodontium album. The fungal entomopathogen community surrounding infected spiders was sampled at different habitats/trophic levels, including soil, leaf litter, leaf, and twig, and analyzed using ITS amplicon sequencing. These data revealed broad but differential distribution of insect-pathogenic fungi between habitats and variation between sites, with members of genera belonging to Metarhizium and Metacordyceps from Clavicipitaceae, Purpureocillium and Polycephalomyces from Ophiocordyceps, and Akanthomyces and Simplicillium from Cordycipitaceae predominating. However, no sequences corresponding to Gibellula or Parengyodontium, even at the genera levels, could be detected. Potential explanations for these findings are discussed. These data highlight novel discovery of fungal spider pathogens and open the broader question regarding the environmental distribution and ecological niches of such host-specific pathogens. 

Abstract Metal halide perovskites based on formamidinium (FA), or FA‐rich compositions have shown great promise for high‐performance photovoltaics. A deeper understanding of the impact of ambient conditions (e.g., moisture, oxygen, and illumination) on the possible reactions of FA‐based perovskite films and their processing sensitivities has become critical for further advances toward commercialization. Herein, we investigate reactions that take place on the surface of the FA_0.7Cs_0.3, mixed Br/I wide bandgap perovskite thin films in the presence of humid air and ambient illumination. The treatment forms a surface layer containing O, OH, and N‐based anions. We propose the latter originates from formamidine trapped at the perovskite/oxide interface reacting further to cyanide and/or formamidinate—an understudied class of pseudohalides that bind to Pb. Optimized treatment conditions improve photoluminescence quantum yield owing to both reduced surface recombination velocity and increased bulk carrier lifetime. The corresponding perovskite solar cells also exhibit improved performance. Identifying these reactions opens possibilities for better utilizing cyanide and amidinate ligands, species that may be expected during vapor processing of FA‐based perovskites. Our work also provides new insights into the self‐healing or self‐passivating of MA‐free perovskite compositions where FA and iodide damage could be partially offset by advantageous reaction byproducts. image 

In this full research paper, we discuss the benefits and challenges of using GPT-4 to perform qualitative analysis to identify faculty’s mental models of assessment. Assessments play an important role in engineering education. They are used to evaluate student learning, measure progress, and identify areas for improvement. However, how faculty members approach assessments can vary based on several factors, including their own mental models of assessment. To understand the variation in these mental models, we conducted interviews with faculty members in various engineering disciplines at universities across the United States. Data was collected from 28 participants from 18 different universities. The interviews consisted of questions designed to elicit information related to the pieces of mental models (state, form, function, and purpose) of assessments of students in their classrooms. For this paper, we analyzed interviews to identify the entities and entity relationships in participant statements using natural language processing and GPT-4 as our language model. We then created a graphical representation to characterize and compare individuals’ mental models of assessment using GraphViz. We asked the model to extract entities and their relationships from interview excerpts, using GPT-4 and instructional prompts. We then compared the results of GPT-4 from a small portion of our data to entities and relationships that were extracted manually by one of our researchers. We found that both methods identified overlapping entity relationships but also discovered entities and relationships not identified by the other model. The GPT-4 model tended to identify more basic relationships, while manual analysis identified more nuanced relationships. Our results do not currently support using GPT-4 to automatically generate graphical representations of faculty’s mental models of assessments. However, using a human-in-the-loop process could help offset GPT-4’s limitations. In this paper, we will discuss plans for our future work to improve upon GPT-4’s current performance. 

In this full research paper, we discuss the benefits and challenges of using GPT-4 to perform qualitative analysis to identify faculty’s mental models of assessment. Assessments play an important role in engineering education. They are used to evaluate student learning, measure progress, and identify areas for improvement. However, how faculty members approach assessments can vary based on several factors, including their own mental models of assessment. To understand the variation in these mental models, we conducted interviews with faculty members in various engineering disciplines at universities across the United States. Data was collected from 28 participants from 18 different universities. The interviews consisted of questions designed to elicit information related to the pieces of mental models (state, form, function, and purpose) of assessments of students in their classrooms. For this paper, we analyzed interviews to identify the entities and entity relationships in participant statements using natural language processing and GPT-4 as our language model. We then created a graphical representation to characterize and compare individuals’ mental models of assessment using GraphViz. We asked the model to extract entities and their relationships from interview excerpts, using GPT-4 and instructional prompts. We then compared the results of GPT-4 from a small portion of our data to entities and relationships that were extracted manually by one of our researchers. We found that both methods identified overlapping entity relationships but also discovered entities and relationships not identified by the other model. The GPT-4 model tended to identify more basic relationships, while manual analysis identified more nuanced relationships. Our results do not currently support using GPT-4 to automatically generate graphical representations of faculty’s mental models of assessments. However, using a human-in-the-loop process could help offset GPT-4’s limitations. In this paper, we will discuss plans for our future work to improve upon GPT-4’s current performance. 

We report on an examination of mobile ion concentration (N₀) in perovskite solar cells (PSCs) as a function of temperature and device architecture. 

Abstract We measure the projected two-point correlation functions of emission-line galaxies (ELGs) from the Dark Energy Spectroscopic Instrument One-Percent Survey and model their dependence on stellar mass and [OII] luminosity. We select ∼180,000 ELGs with redshifts of 0.8 < z < 1.6, and define 27 samples according to cuts in redshift and both galaxy properties. Following a framework that describes the conditional [OII] luminosity–stellar mass distribution as a function of halo mass, we simultaneously model the clustering measurements of all samples at fixed redshift. Based on the modeling result, most ELGs in our samples are classified as central galaxies, residing in halos of a narrow mass range with a typical median of ∼10^12.2−12.4h⁻¹M_⊙. We observe a weak dependence of clustering amplitude on stellar mass, which is reflected in the model constraints and is likely a consequence of the 0.5 dex measurement uncertainty in the stellar mass estimates. The model shows a trend between galaxy bias and [OII] luminosity at high redshift (1.2 < z < 1.6) that is otherwise absent at lower redshifts.