IDEAcology: An interface to streamline and facilitate efficient, rigorous expert elicitation in ecology
Bibliographic record
Abstract
Abstract Here, we demonstrate how IDEAcology aids in preparing for and implementing a structured expert elicitation using the IDEA protocol, an iterative quantitative expert elicitation framework. Expert judgement is used to inform decision‐making on environmental assessment and management when imminent decisions are required, and quantitative data are absent or uninformative. Structured elicitation protocols can help improve the final judgements derived from experts, but they can also be administratively heavy and time‐consuming, requiring manual collation of experts' estimates and rationales, construction and dissemination of summary plots for discussion and collating final estimates post‐discussion. These challenges highlight the need for a centralised portal that enables synchronous access by all contributors, real‐time structured facilitation of discussion, whether in person or online, and streamlined data management. To meet this need, we developed the IDEAcology interface ( www.ideacology.com ) to support data collation, summary, interactions and ultimately the deployment of structured expert elicitation using the IDEA protocol. The IDEAcology interface is designed to be a central portal for scientists and practitioners to easily implement structured expert elicitation projects, while also facilitating data management by providing a reliable and efficient way for elicitation managers to design and run an elicitation, and for experts to input, visualise and cross‐examine estimates. The key advantages that IDEAcology provides include an easy‐to‐use interface with synchronous access to a single platform, reducing logistic difficulties, facilitating transparent discussion, improving the accuracy of estimates, enabling fast and efficient reporting by providing analysis‐ready data outputs and lastly, flexibility in the types of elicitation questions that can be accommodated in the interface.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.079 | 0.027 |
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 source (direct Gemma or distilled Codex), 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".