Dr Heloise Stevance

Abstract:

The volume and complexity of scientific literature are expanding rapidly, making it increasingly difficult to extract and synthesise information across studies. This challenge is particularly acute in the biological sciences, where evidence spans multiple levels of organisation and heterogeneous experimental designs. Large Language Model (LLM) pipelines offer a scalable route to evidence synthesis, but many existing approaches lack transparency, modularity, and effective mechanisms for human oversight. We present MetaBeeAI, an open-source, modular pipeline that integrates established LLM techniques into a coherent, auditable workflow for structured data extraction in biology. MetaBeeAI combines modular prompting, multi-pass extraction, and expert-in-the-loop validation within an interface that presents model outputs alongside source text, enabling inspection, correction, and iterative refinement. The pipeline produces machine-readable records of prompts, configurations, and expert annotations, supporting reproducibility and continuous improvement. We apply MetaBeeAI to 924 research papers on bees and pesticides, extracting structured information on species, compounds, exposure designs, and experimental context. Evaluation demonstrates improved consistency, convergence with expert judgement, and robustness across heterogeneous biological studies, highlighting the value of expert-guided refinement. MetaBeeAI provides a transparent and extensible framework for scalable evidence synthesis, supporting reliable integration of LLMs into biological research workflows.

Abstract:

The Asteroid Terrestrial-impact Last Alert System (ATLAS) is an all-sky optical sky survey with a cadence of 24 to 48 hours (Tonry et al., 2018), and the ATLAS Transient Server (Smith et al., 2020) processes the alert stream to enable the discovery and follow-up of extra-galactic transients. The data from the ATLAS server can be accessed through a REST API, which has allowed the development of bots that need direct access to the data to help rank alerts and trigger follow-up observations of promising targets. Here we present the Python client we have developed for the ATLAS API to help connect bots and scientists to our data.

Abstract:

The launch of the Einstein probe (EP) mission has revolutionized the detection and follow-up observations of fast X-ray transients (FXTs) by providing prompt and timely access to their precise localizations. In the first year of its operation, the EP mission reported the discovery of 72 high signal-to-noise FXTs. Subjected to the visibility in the sky and weather conditions, we search for the optical counterparts of 42 EP-discovered FXTs from the Lulin Observatory. We successfully detected the optical counterparts of 12 FXTs, and five of those were first discovered by us from the Lulin Observatory. We find that the optical counterparts are generally faint (r > 20 mag) and decline rapidly (>0.5 mag day−1). We also find that 12 out of 42 FXTs show direct evidence of their association with gamma-ray bursts (GRBs) through significant temporal and spatial overlapping. Furthermore, the luminosities and redshifts of FXTs with confirmed optical counterparts in our observations are fully consistent with the faintest end of the GRB population. However, the nondetection of any associated optical counterpart with a significant fraction of FXTs suggests that EP FXTs are likely a subset of the so-called “dark FXTs,” similar to “dark GRBs.” Additionally, the luminosities of two FXTs with confirmed redshifts are also consistent with jetted tidal disruption events (TDEs). However, we find that the optical luminosities of FXTs differ significantly from typical supernova shock breakout or kilonova emissions. Thus, we conclude that a significant fraction of EP-discovered FXTs are associated with events having relativistic jets; either a GRB or a jetted TDE.