Transparent Screening for LLM Inference and Training Impacts

This paper presents a transparent screening framework for estimating the inference and training impacts of large language models under limited observability. It aims to improve comparability, transparency, and reproducibility by providing an auditable proxy methodology for opaque proprietary services.