{"id":"W4416748851","doi":"10.1109/iros60139.2025.11246637","title":"TEM <sup>3</sup> -Learning: Time-Efficient Multimodal Multi-Task Learning for Advanced Assistive Driving","year":2025,"lang":"","type":"article","venue":"","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Feature (linguistics); Context (archaeology); Inference; Advanced driver assistance systems; Feature extraction; Limiting; Modality (human–computer interaction); Reinforcement learning; Code (set theory)","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":[0.001227263,0.001395502,0.0008939111,0.0004191176,0.0003932958,0.0009297224,0.003395516,0.001638422,0.008352571],"category_scores_gemma":[0.002104392,0.0004159719,0.001084236,0.0005980529,0.0004090125,0.001647287,0.001615586,0.00184376,0.003781771],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008646955,"about_ca_system_score_gemma":0.001235945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009918459,"about_ca_topic_score_gemma":0.01610757,"domain_scores_codex":[0.9996316,0.00007891583,0.00001917605,0.0001269458,0.00008447964,0.00005878503],"domain_scores_gemma":[0.9995102,0.0001409266,0.00002160561,0.0001259447,0.0001645782,0.00003689975],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004654373,0.000428692,0.001176887,0.0002598903,0.000154688,0.0001593633,0.00008861518,0.2050783,0.01381268,0.004999064,0.05658319,0.7167932],"study_design_scores_gemma":[0.000020445,0.00006419775,0.0002305665,0.000008174557,0.00001132026,0.00003103057,0.00001444074,0.985875,0.004461914,0.003255139,0.006017796,0.000009953364],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02770503,0.001604271,0.9351308,0.0007818873,0.000346406,0.0003282688,0.002722907,0.02349642,0.007883959],"genre_scores_gemma":[0.3653062,0.0008577929,0.594266,0.001083056,0.0002232876,0.0007865123,0.01635227,0.001241927,0.01988298],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009918459,"threshold_uncertainty_score":0.02794218,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01552925536838826,"score_gpt":0.2868858878570651,"score_spread":0.2713566324886768,"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."}}