# AI-assisted Living Evidence Databases for Conservation Science

*2025-10-01 — paper*

Authors: Sadiq Jaffer, William Morgan, Sam Reynolds, Alec Christie, Anil Madhavapeddy, William Sutherland


Living evidence databases offer a robust and dynamic alternative to static
systematic reviews but require a resilient technical infrastructure for
continuous evidence processing.

This working paper describes the architecture and implementation of a complete,
end-to-end pipeline for this purpose, developed initially for the conservation
science domain. Designed to operate on local infrastructure using self-hosted
models, the system ingests and normalizes documents from academic publishers,
screens them for relevance using a multi-stage process, and extracts structured
data according to a predefined schema.

Key features include a hybrid retrieval model; a human-AI collaborative process
for refining inclusion criteria from complex protocols, and the integration of
an established, statistically-principled stopping rule to ensure efficiency. In
a baseline evaluation against a prior large-scale manual review, the fully
automated pipeline achieved 97% recall and identified a significant number of
relevant studies not included in the original review, demonstrating its
viability as a foundational tool for maintaining living evidence databases.

Read more as well on the [CE blog](https://about.conservationevidence.com/2025/10/10/news-2/).


DOI: 10.33774/coe-2025-rmsqf
Classification: preprint
Venue: Cambridge Open Engage
URL: https://www.cambridge.org/engage/coe/article-details/68e52a47bc2ac3a0e0c0eda8
Projects: Conservation Evidence Copilots

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---
Canonical: https://anil.recoil.org/papers/2025-evidence-tap
Type: paper
License: CC BY 4.0 <https://creativecommons.org/licenses/by/4.0/>
Tags: evidence, ai, llms, preprint, ce