{"id":"W4379522641","doi":"10.21428/594757db.e0f8ffcd","title":"Guided Learning of Human Sensor Models with Low-Level Grounding","year":2023,"lang":"en","type":"article","venue":"","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada; Queen's University","keywords":"Interpretability; Representation (politics); Computer science; Artificial intelligence; Machine learning; Raw data; Pattern recognition (psychology)","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":[0.0003316392,0.0001080391,0.0001467247,0.0001849639,0.0002212196,0.00006668641,0.0005415922,0.00003515735,0.00001708066],"category_scores_gemma":[0.00003504699,0.00009003399,0.00003697088,0.0008280866,0.00003647068,0.0002702269,0.0002239889,0.0001825271,0.0001088638],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002366794,"about_ca_system_score_gemma":0.00002776588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007679304,"about_ca_topic_score_gemma":0.000009685609,"domain_scores_codex":[0.9988911,0.00005971644,0.0002129265,0.0003247224,0.000276201,0.0002352977],"domain_scores_gemma":[0.999148,0.000120112,0.0001107724,0.0004634831,0.0000949829,0.00006265053],"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":[0.000002388992,0.00004399101,0.008547578,0.00004197087,0.00002792233,0.000006948193,0.001665648,0.3178508,0.03312493,0.6309541,0.0002782668,0.007455357],"study_design_scores_gemma":[0.0002380317,0.00005232084,0.02458371,0.00002618196,0.000003435147,0.000006476883,0.0000805427,0.9685934,0.001763784,0.004467902,0.00004304833,0.0001411638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4181359,0.000001156184,0.5732946,0.0003674072,0.0000151823,0.00009237623,4.307254e-7,0.0005279031,0.007565086],"genre_scores_gemma":[0.8930677,9.162217e-7,0.1044623,0.00003835394,0.00001899019,0.00002034615,0.000005291154,0.00001453475,0.002371568],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6507426,"threshold_uncertainty_score":0.367148,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07235375822604777,"score_gpt":0.3253710443923826,"score_spread":0.2530172861663348,"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."}}