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Purpose: From an exploratory perspective, this paper aims to assess how well ChatGPT scores in an accounting proficiency exam in Portugal, as well as its overall understanding of the issues, purpose and context underlying the questions under assessment. Design/methodology/approach: A quasi-experimental method is used in this study. The questions from an exam by the Portuguese Order of Chartered Accountants (OCC, in the Portuguese acronym) served as input queries, while the responses (outputs) from ChatGPT were compared with those from the OCC. Findings: The findings indicate that ChatGPT's responses were able to deduce the primary issue underlying the matters assessed, although some responses were inaccurate or imprecise. Also, the tool did not have the same score in all matters, being less accurate in those requiring more professional judgment. The findings also show that the ChatGPT did not pass the exam, although it was close to doing so. Originality: To the best of the authors' knowledge, there is little research on ChatGPT accuracy in accounting proficiency exams, this being the first such study in Portugal. Practical implications: The findings from this research can be useful to accounting professionals to understand how ChatGPT may be used for practitioners, stressing that it could assist them and improve efficiency, but cannot, at least for now, replace the human professional. It also highlights the potential use of ChatGPT as an additional resource in the classroom, encouraging students to engage in critical thinking and facilitating open discussion with the guidance of teachers. Consequently, it can also prove beneficial for academic purposes, aiding in the learning process. [ABSTRACT FROM AUTHOR]

Conserving Nature's Stage (CNS) is a concept from conservation planning that promotes the protection of areas encompassing a broad range of enduring geophysical traits to provide long-term habitat for diverse species. The efficacy of using enduring geophysical characteristics as surrogates for biodiversity, independent of non-geophysical features and when considering finer resolution area selections, has yet to be investigated. Here, we evaluated CNS using 33 fine-scale inventories of vascular plant, non-vascular plant, invertebrate or vertebrate species from 13 areas across three continents. For each inventory, we estimated a continuous multidimensional surrogate defined from topographic and soil estimates of the surveyed plots. We assessed surrogate effectiveness by comparing the species representation of surrogate selected plots to the representation from plots picked randomly and using species information. We then used correlation coefficients to assess the link between the performance and qualities of the inventories, surroundings and surrogates. The CNS surrogate showed positive performance for 24 of the 33 inventories, and among these tests, represented 28 more species than random and 83% of the total number of species on average. We also found a small number of weak correlations between performance and environmental variability, as well as qualities of the surrogate. Our study demonstrates that prioritizing areas for a variety of geophysical characteristics will, in most cases, promote the representation of species. Our findings also point to areas for future research that might enhance CNS surrogacy. This article is part of the Theo Murphy meeting issue 'Geodiversity for science and society'. [ABSTRACT FROM AUTHOR]

We compiled 46 broadscale data sets of species richness for a wide range of terrestrial plant, invertebrate, and ectothermic vertebrate groups in all parts of the world to test the ability of metabolic theory to account for observed diversity gradients. The theory makes two related predictions: (1) In-transformed richness is linearly associated with a linear, inverse transformation of annual temperature, and (2) the slope of the relationship is near -0.65. Of the 46 data sets, 14 had no significant relationship; of the remaining 32, nine were linear, meeting prediction 1. Model I (ordinary least squares, OLS) and model II (reduced major axis, RMA) regressions then tested the linear slopes against prediction 2. In the 23 data sets having nonlinear relationships between richness and temperature, split-line regression divided the data into linear components, and regressions were done on each component to test prediction 2 for subsets of the data. Of the 46 data sets analyzed in their entirety using OLS regression one was consistent with metabolic theory (meeting both predictions), and one was possibly consistent. Using RMA regression, no data sets were consistent. Of 67 analyses of prediction 2 using OLS regression on all linear data sets and subsets, two were consistent with the prediction, and four were possibly consistent. Using RMA regression, one was consistent (albeit weakly), and four were possibly consistent. We also found that the relationship between richness and temperature is both taxonomically and geographically conditional, and there is no evidence for a universal response of diversity to temperature. Meta-analyses confirmed significant heterogeneity in slopes among data sets, and the combined slopes across studies were significantly lower than the range of slopes predicted by metabolic theory based on both OLS and RMA regressions. We conclude that metabolic theory, as currently formulated, is a poor predictor of observed diversity gradients in most terrestrial systems.

Aim: We propose a combined approach to explore the model transferability and the effect of climate change on habitat suitability. Location: Southwest United States. Taxon: Amphibians; Anura; Bufonidae; Anaxyrus microscaphus. Methods: To assess the impacts of climate change on A. microscaphus, we obtained 3162 records from the Arizona Game and Fish Department. We classified occurrence records data into six historical groups (from 1960 to 2018) and a contemporary group (from 2019 to 2022). We investigated the ability of historical data to generate accurate predictions of the species' contemporary distribution. We used species distribution modelling to compute habitat suitability values and identified spatial clusters of high suitability values (using hotspot analyses). Results: We show that historical occurrences successfully predicted the contemporary distribution of A. microscaphus. We demonstrate that the geographical distribution of contemporary hotspots is strongly related to precipitation of the wettest quarter. This research provides evidence that climate change will likely affect the distribution of hotspots and the habitat suitability of A. microscaphus. Losses of 37%–48% contraction are expected at the margins of the range both in the north and south of the geographic limits. Main Conclusions: We show that recent past data can be used to predict contemporary species distribution accurately. We highlight that habitat suitability values of A. microscaphus are strongly related to climate, topography and distance to streams. This study provides clear evidence of the negative impacts of climate change on habitat suitability in the Southwest United States drylands. Our approach has applied utility to managers to target survey areas, monitor habitat changes, plan for conservation mitigation measures, conserve stream flows across the species' range and inform recovery if needed. Thus, managers could use previously collected data to predict contemporary species distribution when their occurrence accurately characterizes the species' ecological niche. [ABSTRACT FROM AUTHOR]

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The gaps observed in entities' traditional reports and accounts led to the emergence of the integrated report (IR), which includes several content elements, namely the component relating to risks and opportunities. Within this scope, the specific risks that may affect an organization's capacity to create value are disclosed, among others, which is information of interest to the different stakeholders. This paper aims to identify the explanatory factors that influence the disclosure of risks in IRs. For this purpose, the IRs of entities listed on the Brazilian stock exchange for the year 2020 were assessed. The study was based on the explanatory theories of risk disclosure usually found in the literature, namely, the legitimacy, the agency, the signaling, and the upper echelon theories. Linear regression models were used with the disclosure rates of different types of risk as dependent variables. The size, profitability, indebtedness, independence, and gender diversity in the board of directors (BD), audit, and activity sector comprised the selected explanatory factors. Associations were found between some of the types of risks disclosed and the size of the entity, the existence of an audit, the independence of the BD, and the activity sector. The paper contributes to the literature about the explanatory factors of risk disclosure by exploring its analysis with different typologies and attributes, having the IR as a source of information, which is still little explored. The scientific contribution encompasses proposing a new risk analysis model in the IR. The innovative elements also comprise the classification of risks related to sustainable development (SD), including environmental, social, and governance (ESG) factors. [ABSTRACT FROM AUTHOR]