{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000239555,0.0008726423,0.0004005832,0.0004164421,0.0005054833,0.0006168622,0.001614248,0.0006919486,0.01120682],"category_scores_gemma":[0.001167079,0.0002993134,0.0004068693,0.0004443503,0.0003926245,0.0008365462,0.001027902,0.0009726742,0.002277835],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006131124,"about_ca_system_score_gemma":0.0009329289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003050857,"about_ca_topic_score_gemma":0.005958145,"domain_scores_codex":[0.9997883,0.00003277827,0.00001078232,0.00005004214,0.00008558127,0.00003253969],"domain_scores_gemma":[0.9996766,0.00009473144,0.00002996182,0.000111609,0.00005883782,0.0000283233],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0007322382,0.0002458308,0.002207975,0.0005958757,0.000144808,0.0004587835,0.0003575512,0.3573095,0.07903735,0.03520996,0.05422864,0.4694715],"study_design_scores_gemma":[0.00005973733,0.00008940788,0.0003257757,0.00001629189,0.00001339846,0.0001092173,0.00004742633,0.9378432,0.03316059,0.008052134,0.02025863,0.00002404937],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02363364,0.0002233948,0.934083,0.000214705,0.000128117,0.0001027929,0.0003565288,0.03526634,0.005991403],"genre_scores_gemma":[0.2914698,0.0001210968,0.6959936,0.0002153259,0.00003796652,0.0002647527,0.001012807,0.002821965,0.008062717],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01120682,"threshold_uncertainty_score":0.03749061,"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."}}