NITA-Qrels — Relevance Judgment subtask

Is this a manual (human intervention) or automatic run?: automatic
Does this run leverage neural networks?: yes
Does this run leverage proprietary models in any step of the retrieval pipeline?: no
Does this run leverage open-weight LLMs (> 5B parameters) in any step of the retrieval pipeline?: no
Does this run leverage smaller open-weight language models in any step of the retrieval pipeline?: yes
What would you categorize this run as?: Multi-Stage Pipeline pointwise
Please provide a short description of this run: This run uses a multi-stage retrieval pipeline. Candidate documents are first taken from a baseline dense retrieval run (top-100), then restricted to the top-20 per query. These candidates are reranked using the BAAI/bge-reranker-large cross-encoder model, which outputs confidence scores mapped into 5-level relevance judgments (0–4). Final results are written in TREC QREL TSV format with per-query top-20 judgments.
Please give this run a priority for inclusion in manual assessments.: 5 (bottom)