{"id":"W3169048261","doi":"10.21428/594757db.efb4fcff","title":"Driving Scene Understanding: How much temporal context and spatial resolution is necessary?","year":2021,"lang":"en","type":"article","venue":"","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Variety (cybernetics); Context (archaeology); Metadata; Field (mathematics); Modalities; Spatial contextual awareness; Modality (human–computer interaction); Human–computer interaction; Action (physics); Artificial intelligence; Data science; Computer vision; World Wide Web; Geography","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001704615,0.00009633342,0.000106236,0.00006171777,0.0001647959,0.0007596268,0.00028688,0.00003202789,0.00005944633],"category_scores_gemma":[0.0000207538,0.00009028699,0.00002669058,0.0002389829,0.00003770563,0.001043598,0.0005796338,0.00006548907,0.00001107796],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000428975,"about_ca_system_score_gemma":0.00003000947,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001645889,"about_ca_topic_score_gemma":0.0004962739,"domain_scores_codex":[0.9990665,0.00003657189,0.00009979143,0.0003786708,0.0002151238,0.0002033709],"domain_scores_gemma":[0.9994876,0.00002936214,0.0000432976,0.0003340156,0.00003502428,0.00007065388],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007170927,0.0001323299,0.0256759,0.0000537924,0.00009484196,0.0002368319,0.000961279,0.000002269979,0.0006184494,0.5299756,0.1749635,0.267278],"study_design_scores_gemma":[0.001595663,0.0001259593,0.006338337,0.00008843607,0.00002399583,0.00003155929,0.004268208,0.9037234,0.005311245,0.02807233,0.04976648,0.0006543403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002043358,0.0001193983,0.9733901,0.01876102,0.0002907869,0.00006568289,0.000002066578,0.0001046965,0.005222878],"genre_scores_gemma":[0.9709949,0.00004337326,0.02408214,0.0007336685,0.00007306888,0.000001654602,0.00001667091,0.000004943713,0.004049618],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9689515,"threshold_uncertainty_score":0.7325099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04808237679590738,"score_gpt":0.2487900674462667,"score_spread":0.2007076906503593,"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."}}