This document is the technical specification for DOI 1. 9 of the Merutan Theory series. It documents two unedited screen-capture videos, each showing a SQL script executed live against the source database for one of two AI upscaling models (Upscayl High-Fidelity: 142, 868, 480 full / 255, 518 flat-region-filtered blocks; Upscayl Digital-Art: 163, 971, 072 full / 2, 139, 591 flat-region-filtered blocks), computing these dataset block statistics and Failure Fingerprint (, ) values for each model. The purpose is to provide direct evidence that these values were computed via live SQL execution against the source data, not fabricated or adjusted post hoc. These Failure Fingerprint values (, ) suggest potential applicability for identifying quality differences between model versions, providing a practical foundation for model version management in real-world AI pipelines. Dataset and Mandatory Citation: - Source: NIH ChestX-ray8 (Hospital-scale chest x-ray database) - Citation: Wang, X. , Peng, Y. , Lu, L. , Lu, Z. , Bagheri, M. , & Summers, R. M. (2017). "ChestX-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thoracic diseases. " Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3462–3471. - Download: https: //nihcc. app. box. com/v/ChestXray-NIHCC Contact: s. shiny. n. works@gmail. com
neco mohumohu (2026) studied this question.