{"id":"W2330370478","doi":"10.2514/6.2012-2440","title":"Bayesian Decision Making for Planetary Micro-Rovers","year":2012,"lang":"en","type":"article","venue":"Infotech@Aerospace 2012","topic":"AI-based Problem Solving and Planning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Bayesian probability; Computer science; Artificial intelligence; Astrobiology; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008215372,0.0002665158,0.000239934,0.0001539051,0.0003170115,0.0001921767,0.0008146327,0.0002274388,0.000053659],"category_scores_gemma":[0.0001153683,0.0002549515,0.0001159709,0.0002763343,0.00003796064,0.001509877,0.0002171268,0.0002783807,0.0002288496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000736945,"about_ca_system_score_gemma":0.00006285237,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000339158,"about_ca_topic_score_gemma":0.00001161762,"domain_scores_codex":[0.9981374,0.00004282093,0.0002886564,0.0003465287,0.0003015485,0.0008830338],"domain_scores_gemma":[0.9981662,0.0006800956,0.0001826046,0.000690248,0.00005546926,0.0002254004],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002838146,0.0002769489,0.1028786,0.0002943566,0.0001866306,0.00003286391,0.007798708,0.007505996,0.00707714,0.01957797,0.4675781,0.3865089],"study_design_scores_gemma":[0.003010553,0.0006271687,0.02878067,0.001222586,0.0001060334,0.0002884226,0.0002673542,0.09882781,0.01035235,0.008489863,0.8450519,0.002975254],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01005368,0.001456277,0.9847539,0.0006456047,0.001261741,0.0003184131,0.00002423735,0.0003671309,0.001119004],"genre_scores_gemma":[0.565224,0.00001037627,0.4332609,0.000825526,0.0002774163,0.00002048884,0.00001612076,0.00002405562,0.0003410973],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5551703,"threshold_uncertainty_score":0.9999903,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01419621026347043,"score_gpt":0.2593444074855263,"score_spread":0.2451481972220559,"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."}}