{"id":"W4404133172","doi":"10.1145/3649329.3658269","title":"CLUMAP: Clustered Mapper for CGRAs with Predication","year":2024,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Parallel computing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003644542,0.00006012521,0.00004837713,0.00004556752,0.00001959673,0.0000504109,0.00003084703,0.00003762864,0.00005977363],"category_scores_gemma":[0.000003309167,0.00004591792,0.00001908867,0.00009030513,0.000007669658,0.00005838553,0.00000274732,0.00003080568,0.00003885966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000231641,"about_ca_system_score_gemma":0.000008857626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003365969,"about_ca_topic_score_gemma":0.00001499651,"domain_scores_codex":[0.999671,0.000002682361,0.00008227608,0.00009020177,0.00006318223,0.00009068008],"domain_scores_gemma":[0.9998394,0.0000217059,0.000003584584,0.00008534023,0.00002213193,0.00002783769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001385279,0.00001269362,0.000137531,0.0004589025,0.00007339209,0.000002302269,0.0002058038,0.9252438,0.001773608,0.01765713,0.04213756,0.0122834],"study_design_scores_gemma":[0.0001207115,0.00003279048,0.0001072429,0.00003282405,0.00001402246,0.00000196855,0.00001950349,0.9633294,0.001504601,0.0002097479,0.03455182,0.00007536449],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00419197,0.0001339887,0.9885287,0.0002529129,0.0001889199,0.0002099816,0.000008030175,0.0004961976,0.005989278],"genre_scores_gemma":[0.9877849,0.00002093807,0.01021525,0.00006325866,0.0001040456,0.00003381776,0.0001033904,0.00003571557,0.001638678],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9835929,"threshold_uncertainty_score":0.1872478,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004876371221181485,"score_gpt":0.1866193269581754,"score_spread":0.1817429557369939,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}