Expert Knowledge Elicitation: Accessing the Big Data in Experts’ Brains
Bibliographic record
Abstract
A vast amount of expert knowledge currently remains inaccessible to digital information systems. Expert knowledge elicitation is a systematic approach to accessing and synthesizing the insights of subject matter experts, especially when available objective data are incomplete. In plant pathology, expert knowledge elicitation is valuable for addressing urgent, uncertain, and/or future challenges, such as emerging disease threats, complex epidemiological systems, knowledge gaps when resources are limited, and future scenarios. This perspective explores when expert knowledge elicitation is most effective for addressing plant health challenges, emphasizing its role in informing timely, expert-based decisions. We discuss lessons learned from real-world implementations across diverse regions and pathosystems, highlighting strategies for eliciting, structuring, and interpreting expert-derived data, as well as associated caveats. We frame expert knowledge as a form of big data and outline how existing big-data streams (e.g., remote sensing, crowdsourced reports, and digital surveillance) can inform expert judgements. Outputs from expert knowledge elicitation can be captured as scalable datasets (text, tabular, audio, and video) that enable artificial intelligence-supported synthesis. We illustrate how expert knowledge can be integrated in Bayesian analyses, providing a transparent and rigorous approach to understanding uncertainty and improving inference. Finally, we outline future opportunities, including integration with artificial intelligence, to scale and strengthen expert knowledge elicitation in support of global plant health. [Formula: see text] Copyright © 2025 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".