AI Chat Assistants can Improve Conversations about Divisive Topics

A rapidly increasing amount of human conversation occurs online. But divisiveness and conflict can fester in text-based interactions on social media platforms, in messaging apps, and on other digital forums. Such toxicity increases polarization and, importantly, corrodes the capacity of diverse societies to develop efficient solutions to complex social problems that impact everyone. Scholars and civil society groups promote interventions that can make interpersonal conversations less divisive or more productive in offline settings, but scaling these efforts to the amount of discourse that occurs online is extremely challenging. We present results of a large-scale experiment that demonstrates how online conversations about divisive topics can be improved with artificial intelligence tools. Specifically, we employ a large language model to make real-time, evidence-based recommendations intended to improve participants' perception of feeling understood in conversations. We find that these interventions improve the reported quality of the conversation, reduce political divisiveness, and improve the tone, without systematically changing the content of the conversation or moving people's policy attitudes. These findings have important implications for future research on social media, political deliberation, and the growing community of scholars interested in the place of artificial intelligence within computational social science

Towards Coding Social Science Datasets with Language Models

Researchers often rely on humans to code (label, annotate, etc.) large sets of texts. This kind of human coding forms an important part of social science research, yet the coding process is both resource intensive and highly variable from application to application. In some cases, efforts to automate this process have achieved human-level accuracies, but to achieve this, these attempts frequently rely on thousands of hand-labeled training examples, which makes them inapplicable to small-scale research studies and costly for large ones. Recent advances in a specific kind of artificial intelligence tool - language models (LMs) - provide a solution to this problem. Work in computer science makes it clear that LMs are able to classify text, without the cost (in financial terms and human effort) of alternative methods. To demonstrate the possibilities of LMs in this area of political science, we use GPT-3, one of the most advanced LMs, as a synthetic coder and compare it to human coders. We find that GPT-3 can match the performance of typical human coders and offers benefits over other machine learning methods of coding text. We find this across a variety of domains using very different coding procedures. This provides exciting evidence that language models can serve as a critical advance in the coding of open-ended texts in a variety of applications

Evaporation and carbonic anhydrase activity recorded in oxygen isotope signatures of net CO2 fluxes from a Mediterranean soil

The oxygen stable isotope composition (d18O) of CO2 is a valuable tool for studying the gas exchange between terrestrial ecosystems and the atmosphere. In the soil, it records the isotopic signal of water pools subjected to precipitation and evaporation events. The d18O of the surface soil net CO2 flux is dominated by the physical processes of diffusion of CO2 into and out of the soil and the chemical reactions during CO2–H2O equilibration. Catalytic reactions by the enzyme carbonic anhydrase, reducing CO2 hydration times, have been proposed recently to explain field observations of the d18O signatures of net soil CO2 fluxes. How important these catalytic reactions are for accurately predicting large-scale biosphere fluxes and partitioning net ecosystem fluxes is currently uncertain because of the lack of field data. In this study, we determined the d18O signatures of net soil CO2 fluxes from soil chamber measurements in a Mediterranean forest. Over the 3 days of measurements, the observed d18O signatures of net soil CO2 fluxes became progressively enriched with a well-characterized diurnal cycle. Model simulations indicated that the d18O signatures recorded the interplay of two effects: (1) progressive enrichment of water in the upper soil by evaporation, and (2) catalytic acceleration of the isotopic exchange between CO2 and soil water, amplifying the contributions of ‘atmospheric invasion’ to net signatures. We conclude that there is a need for better understanding of the role of enzymatic reactions, and hence soil biology, in determining the contributions of soil fluxes to oxygen isotope signals in atmospheric CO2

Aquilegia, Vol. 21 No. 2-4, April-December 1997: Newsletter of the Colorado Native Plant Society

https://epublications.regis.edu/aquilegia/1083/thumbnail.jp

Temperature‐sensitive biochemical 18^{18}O‐fractionation and humidity‐dependent attenuation factor are needed to predict δ 18^{18}O of cellulose from leaf water in a grassland ecosystem

We explore here our mechanistic understanding of the environmental and physiological processes that determine the oxygen isotope composition of leaf cellulose (δ$^{18}$O$_{cellulose}$) in a drought‐prone, temperate grassland ecosystem. A new allocation‐and‐growth model was designed and added to an $^{18}$O‐enabled soil–vegetation–atmosphere transfer model (MuSICA) to predict seasonal (April–October) and multi‐annual (2007–2012) variation of δ$^{18}$O$_{cellulose}$ and $^{18}$O‐enrichment of leaf cellulose (Δ$^{18}$O$_{cellulose}$) based on the Barbour–Farquhar model. Modelled δ$^{18}$O$_{cellulose}$ agreed best with observations when integrated over c. 400 growing‐degree‐days, similar to the average leaf lifespan observed at the site. Over the integration time, air temperature ranged from 7 to 22°C and midday relative humidity from 47 to 73%. Model agreement with observations of δ$^{18}$O$_{cellulose}$ (R$^{2}$ = 0.57) and Δ$^{18}$O$_{cellulose}$ (R$^{2}$ = 0.74), and their negative relationship with canopy conductance, was improved significantly when both the biochemical $^{18}$O‐fractionation between water and substrate for cellulose synthesis (ε$_{bio}$, range 26–30‰) was temperature‐sensitive, as previously reported for aquatic plants and heterotrophically grown wheat seedlings, and the proportion of oxygen in cellulose reflecting leaf water $^{18}$O‐enrichment (1 – p$_{ex}$p$_{x}$, range 0.23–0.63) was dependent on air relative humidity, as observed in independent controlled experiments with grasses. Understanding physiological information in δ$^{18}$O$_{cellulose}$ requires quantitative knowledge of climatic effects on p$_{ex}$p$_{x}$ and ε$_{bio}$

Carbon isotope discrimination during branch photosynthesis of Fagus sylvatica: field measurements using laser spectrometry

Photosynthetic carbon isotope discrimination of Fagus sylvatica was measured online and under field conditions using branch bags and laser spectrometers. A substantial variability was observed. Its potential drivers were investigate

A new generation of sensors and monitoring tools to support climate-smart forestry practices

Climate-smart forestry (CSF) is an emerging branch of sustainable adaptive forest management aimed at enhancing the potential of forests to adapt to and mitigate climate change. It relies on much higher data requirements than traditional forestry. These data requirements can be met by new devices that support continuous, in situ monitoring of forest conditions in real time. We propose a comprehensive network of sensors, i.e., a wireless sensor network (WSN), that can be part of a worldwide network of interconnected uniquely addressable objects, an Internet of Things (IoT), which can make data available in near real time to multiple stakeholders, including scientists, foresters, and forest managers, and may partially motivate citizens to participate in big data collection. The use of in situ sources of monitoring data as ground-truthed training data for remotely sensed data can boost forest monitoring by increasing the spatial and temporal scales of the monitoring, leading to a better understanding of forest processes and potential threats. Here, some of the key developments and applications of these sensors are outlined, together with guidelines for data management. Examples are given of their deployment to detect early warning signals (EWS) of ecosystem regime shifts in terms of forest productivity, health, and biodiversity. Analysis of the strategic use of these tools highlights the opportunities for engaging citizens and forest managers in this new generation of forest monitoring.Peer reviewe

A new generation of sensors and monitoring tools to support climate-smart forestry practices

Climate-smart forestry (CSF) is an emerging branch of sustainable adaptive forest management aimed at enhancing the potential of forests to adapt to and mitigate climate change. It relies on much higher data requirements than traditional forestry. These data requirements can be met by new devices that support continuous, in situ monitoring of forest conditions in real time. We propose a comprehensive network of sensors, i.e., a wireless sensor network (WSN), that can be part of a worldwide network of interconnected uniquely addressable objects, an Internet of Things (IoT), which can make data available in near real time to multiple stakeholders, including scientists, foresters, and forest managers, and may partially motivate citizens to participate in big data collection. The use of in situ sources of monitoring data as ground-truthed training data for remotely sensed data can boost forest monitoring by increasing the spatial and temporal scales of the monitoring, leading to a better understanding of forest processes and potential threats. Here, some of the key developments and applications of these sensors are outlined, together with guidelines for data management. Examples are given of their deployment to detect early warning signals (EWS) of ecosystem regime shifts in terms of forest productivity, health, and biodiversity. Analysis of the strategic use of these tools highlights the opportunities for engaging citizens and forest managers in this new generation of forest monitoring.Peer reviewe

Historical aerial surveys map long-term changes of forest cover and structure in the Central Congo basin

Given the impact of tropical forest disturbances on atmospheric carbon emissions, biodiversity, and ecosystem productivity, accurate long-term reporting of Land-Use and Land-Cover (LULC) change in the pre-satellite era (<1972) is an imperative. Here, we used a combination of historical (1958) aerial photography and contemporary remote sensing data to map long-term changes in the extent and structure of the tropical forest surrounding Yangambi (DR Congo) in the central Congo Basin. Our study leveraged structure-from-motion and a convolutional neural network-based LULC classifier, using synthetic landscape-based image augmentation to map historical forest cover across a large orthomosaic (similar to 93,431 ha) geo-referenced to similar to 4.7 +/- 4.3 m at submeter resolution. A comparison with contemporary LULC data showed a shift from previously highly regular industrial deforestation of large areas to discrete smallholder farming clearing, increasing landscape fragmentation and providing opportunties for substantial forest regrowth. We estimated aboveground carbon gains through reforestation to range from 811 to 1592 Gg C, partially offsetting historical deforestation (2416 Gg C), in our study area. Efforts to quantify long-term canopy texture changes and their link to aboveground carbon had limited to no success. Our analysis provides methods and insights into key spatial and temporal patterns of deforestation and reforestation at a multi-decadal scale, providing a historical context for past and ongoing forest research in the area