{"id":"W4224231654","doi":"10.1111/risa.13930","title":"Subjective machines: Probabilistic risk assessment based on deep learning of soft information","year":2022,"lang":"en","type":"article","venue":"Risk Analysis","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Machine learning; Probabilistic logic; Artificial intelligence; Computer science; Heuristics; Gradient boosting; Risk assessment; Process (computing); Random forest","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":[0.003926835,0.0009718224,0.0008433963,0.00134915,0.0002934305,0.001348945,0.001142418,0.0009495523,0.002687013],"category_scores_gemma":[0.009791789,0.0003888288,0.0007345942,0.0006404079,0.0009062872,0.0019827,0.001554216,0.001524217,0.0003709895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00120711,"about_ca_system_score_gemma":0.0008119812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002094398,"about_ca_topic_score_gemma":0.00248946,"domain_scores_codex":[0.9986668,0.0006336646,0.0000684361,0.0001866427,0.0003572609,0.00008718012],"domain_scores_gemma":[0.9958262,0.002751628,0.0004554007,0.0002426585,0.0005638452,0.0001602207],"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":[0.0001503578,0.0001028755,0.003517974,0.0001070997,0.00009615799,0.0001056952,0.0001551959,0.8442913,0.001409307,0.03387044,0.002386803,0.1138068],"study_design_scores_gemma":[0.000002702208,0.00002079087,0.0002374839,0.000009893218,0.000005319434,0.00001209698,0.00000634345,0.9819034,0.0003262509,0.01722801,0.0002414361,0.000006401476],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0290587,0.0002396735,0.968039,0.0003750397,0.00003080847,0.00004605208,0.0001143172,0.0004141159,0.001682284],"genre_scores_gemma":[0.8376401,0.000334717,0.1579733,0.0002767249,0.00007630057,0.0001609056,0.0004065904,0.00009636011,0.00303499],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003926835,"threshold_uncertainty_score":0.02076733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01601607476966815,"score_gpt":0.3191089711325679,"score_spread":0.3030928963628997,"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."}}