Evaluation Reports for TEBT-2025-0018 | Zhang et al.

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Authors: Paul Whaley
Cited by: 0

Abstract

Evaluation report for "AI-assisted Automation for Converting Historical Documents into Structured Database: A Feasibility Study of Environmental Application in Michigan" (Zhang et al.) This manuscript tackles the challenge of transforming legacy (physical) environmental reports into machine-readable data. It presents an analysis of the feasibility of using the CLEAR-Extract pipeline that combines optical character recognition, AI-driven table reconstruction, and structured human oversight. The manuscript focuses on a specific problem area and context without attempting to investigate the generalisability or scaleability of the pipeline. This is legitimate given the resources the authors were working with, making the manuscript a modest but nonetheless useful contribution to the literature. The reviewer recommending major revisions was unfortunately not available to check the revised manuscript. I (the editor) therefore reviewed the manuscript myself. While I was expecting quite substantial revisions after peer-review, the authors responded to reviewer comments without making extensive changes to the manuscript. The authors argued that, because this is a feasibility study without a goal of measuring performance or generalisability of the pipeline, detailed performance metrics, full reporting of prompts, and other such information, may be desirable but is not essential. I agree. I have also not been strict on requiring pipeline code and supporting documentation, as this appears to be held by the NIEHS until specific institutional procedures have been applied. This is not ideal but, given some of the challenges around working with US Federal institutions at the present time, and that some data can anyway only be made available on request for ethical reasons, we will allow the authors to make all data available on request and encourage them to upload what they can to the Zenodo record of their preprint once it has been released by the funder. Overall, I conclude that the revisions the authors have made are sufficient, and I am therefore pleased to accept the manuscript for publication.

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
1 July 2026