From ignorance to familiarity: Contextual knowledge and the field researcher

Protocol for the use of an Optical Nitrate Nitrogen (NO3-N) sensor for measuring ground and surface water NO3-N concentrations

Optical nitrate-nitrogen (NO3-N) sensors are used in environmental monitoring for the real-time detection of dissolved inorganic nitrate and are readily available and increasingly affordable for use by non-experts and may eventually replace the need for expensive laboratory analysis. Many different manufacturers have developed their own instruments for use as permanent in situ sensors in groundwater bores, or as portable ex situ units. The advantage of these NO3-N sensors is that they can be deployed to complement traditional discrete sampling programmes and significantly improve temporal data resolution and provide high resolution data that captures the rate that NO3-N may naturally vary in the environment. However, the potential over dependence on technology i.e. a plug and play approach without careful development of quality assurance protocols can easily lead to poor data outcomes. Thus, the effective use of an optical NO3-N sensor, especially in community-led science, requires specific sensor protocols for its effective use, including:•A regimen of cross checks relative to known standards and/or independently verified laboratory results;•the collection of metadata to contextualise the results; and,•the need for Quality Assurance and Quality Control (QAQC) protocols to provide confidence in the data.

Based on Medicine, The Now and Future of Large Language Models

OBJECTIVES

This review explores the potential applications of large language models (LLMs) such as ChatGPT, GPT-3.5, and GPT-4 in the medical field, aiming to encourage their prudent use, provide professional support, and develop accessible medical AI tools that adhere to healthcare standards.

METHODS

This paper examines the impact of technologies such as OpenAI's Generative Pre-trained Transformers (GPT) series, including GPT-3.5 and GPT-4, and other large language models (LLMs) in medical education, scientific research, clinical practice, and nursing. Specifically, it includes supporting curriculum design, acting as personalized learning assistants, creating standardized simulated patient scenarios in education; assisting with writing papers, data analysis, and optimizing experimental designs in scientific research; aiding in medical imaging analysis, decision-making, patient education, and communication in clinical practice; and reducing repetitive tasks, promoting personalized care and self-care, providing psychological support, and enhancing management efficiency in nursing.

RESULTS

LLMs, including ChatGPT, have demonstrated significant potential and effectiveness in the aforementioned areas, yet their deployment in healthcare settings is fraught with ethical complexities, potential lack of empathy, and risks of biased responses.

CONCLUSION

Despite these challenges, significant medical advancements can be expected through the proper use of LLMs and appropriate policy guidance. Future research should focus on overcoming these barriers to ensure the effective and ethical application of LLMs in the medical field.