gm27q-LMART-1000 — Retrieval Task

Please describe in details how this run was generated: Use trained lambdaMART reranker to rerank all retrieval results from LLM, sparse and dense retrieval. Take the top 1000. Then use the Gemma 27B quantized model to rerank the top 500.
Specify datasets used in this run.: ["This year's TREC TOT training data"]
(if you checked "other", describe here)
Are you 100% confident that no data from https://github.com/microsoft/Tip-of-the-Tongue-Known-Item-Retrieval-Dataset-for-Movie-Identification or iRememberThisMovie.com (besides the training data provided as part of this year's track) was used for producing this run (including any data used for pretraining models that you are building on top of)?: no
Did you use any of the official baseline runs in any way to produce this run?: yes
If you did use any of the official baseline runs in any way to produce this run, please describe how below in sufficient detail (e.g., as reranking candidates or in ensemble with other approaches).: Use pyterrier results as reranking candidates.