Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2509.19393

MetaQuestion: A web application for expert knowledge elicitation addressing plant health and applied plant ecology

Robert Fontan·Christopher M. Perez·Ashish Adhikari·Romaric A. Mouafo-Tchinda·Aaron I. Plex Sulá·Jacobo Robledo·Berea A. Etherton·Manoj Choudhary·Muhammad Aqeel Sarwar·Zunaira Afzal Naveed·Karen A. Garrett
View original source ↗Read source document ↗Export metadata ↓

Abstract

1. Expert knowledge elicitation provides information to characterize ecological systems and management options. Linking expert knowledge elicitation with a curated question catalog supports a community of practice for ongoing improvement of question quality. 2. The MetaQuestion web app we introduce here draws on the PlantQuest catalog of questions addressing applied plant ecology and management options, making the catalog available in a flexible form for organizers of expert knowledge elicitation. Organizers can select among questions in the catalog, modify them as needed, and generate an instrument customized to their elicitation project. MetaQuestion makes available PlantQuest questions specialized for the study of invasive species such as pathogens and arthropod pests, such as geographic analyses of prevalence and network analysis of the movement of plant materials. 3. Experts answer questions in the customized instrument and their responses are compiled. For settings where internet access may be sporadic, there are options to download the instrument for experts' work and then upload responses later. MetaQuestion provides the resulting dataset in a CSV file for analysis in users' choice of software 4. Development of the PlantQuest catalog and the MetaQuestion app is ongoing, incorporating lessons learned from applications of the app. The MetaQuestion app could also be adapted to address questions from other subject areas.

Explore related subjects

q-bio.OT

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Robert Fontan, Christopher M. Perez, Ashish Adhikari, Romaric A. Mouafo-Tchinda, Aaron I. Plex Sulá, Jacobo Robledo, Berea A. Etherton, Manoj Choudhary, Muhammad Aqeel Sarwar, Zunaira Afzal Naveed, Karen A. Garrett. 2025-09-22. MetaQuestion: A web application for expert knowledge elicitation addressing plant health and applied plant ecology. https://arxiv.org/abs/2509.19393

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

MarkerScout: A Disease-Agnostic Machine Learning Framework for Biomarker Prediction from Multi-Scale Mechanistic Models

We demonstrate the framework on three infectious diseases derived from a companion mechanistic immune-simulation platform: SARS-CoV-2, Influenza A Virus, and Plasmodium falciparum. Each disease was evaluated across hospitalization and intensive care unit cohorts, yielding six cohorts in total. Best-pipeline cross-validated macro F1 ranged from 0.82 for IAV-HOSP to 0.99 for COV-ICU, and the framework produced tiered, direction-aware biomarker lists for each disease and phase. Interleukin-18 (IL-18) reached the strongest tier in both SARS-CoV-2 phases with consistent direction. When benchmarked against three separate, independently collected clinical ICU datasets, MarkerScout's top-ranked features outperformed 94.4% of randomly selected feature sets of equivalent size for SARS-CoV-2, with a weaker but directionally consistent advantage for Influenza A Virus (66.7%) and Plasmodium falciparum (60.7%).

q-bio.OT↗
arXiv2026

Enhancing Clinical Decision Support and Differential Diagnosis with Knowledge Graphs, and Retrieval Augmented Generation in Generative AI

Diagnostic error carries a burden, while unconstrained large language models (LLMs) remain vulnerable to hallucination and weak integration of quantitative laboratory dynamics. We developed a decision-support pipeline combining disease-specific biomarker correlation graphs, ordinary differential equations (ODEs), deep sequence classification, and retrieval-augmented generation (RAG). For 103 disease classes from a full blood count (FBC) repository, biomarker networks were used as coupling matrices to generate 30 trajectories per disease (3,090 total). A one-dimensional convolutional neural network (CNN) and long short-term memory (LSTM) network classified disease trajectories and six dynamical clusters. A constrained GPT-4o-mini RAG layer used a 19-pattern BMJ Best Practice/NICE corpus to generate differential diagnoses evaluated for diagnostic suitability, evidential grounding, and clinical plausibility. Across five random-seed runs, disease-level accuracy was $0.940 \pm 0.006$ for the CNN (95\% CI 0.933--0.948) and $0.852 \pm 0.019$ for the LSTM (95\% CI 0.828--0.875); the CNN advantage was 8.87 percentage points (95\% CI 6.47--11.27; $t(4)=10.26$, $p=5.1\times10^{-4}$; Hedges' $g=3.67$). Among 100 sampled RAG cases, 96 parsed successfully; evidence was cited in 97.9\%, the true diagnosis was mentioned in 71.9\%, and the composite score was 3.82/5 with a 47.9\% strict pass rate. The central finding was a decoupling between grounding and diagnostic correctness: classifier-correct versus classifier-wrong outputs differed in diagnostic suitability but not evidential grounding. Post-hoc analysis confirmed a 1.02-point diagnostic-score difference (Mann--Whitney $p=0.0024$; Hedges' $g=0.72$), whereas grounding differed by only $-0.02$ points ($p=0.839$; $g=-0.04$).

q-bio.OT↗
arXiv2026

Expanding the Human Ancestry Ontology to include under-represented populations and ethnicities for broader utility in annotations

Successful discovery, integration and reuse of data relies on the availability of rich, well-structured and machine-readable metadata to describe every aspect of the data, from sample sources to collection processes to experimental protocols. The use of standardised terminologies to express concepts in a harmonised fashion lies at the core of high-quality data annotation, increasing the FAIRness of the data, facilitating data integration and promoting reproducibility. Here, we describe the Human Ancestry Ontology (HANCESTRO), originally developed to improve standardised reporting of genetic ancestry genomic resources such as the NHGRI-EBI GWAS Catalog and the Human Cell Atlas through high-level population descriptors, and more recently expanded to include diverse and previously under-represented populations in genomics and genetics research. HANCESTRO provides a framework for population descriptors that includes both ancestry based on the analysis of genetic information and self-reported ethnicity, which is based on social and cultural factors that don't necessarily align with genetic populations. By enabling the accurate and interoperable representation of population-related data, it promotes inclusive, representative and reproducible science.

q-bio.OT↗