{"id":"W2898354798","doi":"10.1002/rob.21833","title":"Data‐driven mobility risk prediction for planetary rovers","year":2018,"lang":"en","type":"article","venue":"Journal of Field Robotics","topic":"Soil Mechanics and Vehicle Dynamics","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Shared Services Canada; Concordia University","funders":"Canadian Space Agency","keywords":"Slip (aerodynamics); Predictability; Terrain; Computer science; Mobility model; Geology; Engineering; Mathematics; Distributed computing; Aerospace engineering; Geography; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008187036,0.0005609577,0.0004208764,0.0008837648,0.0002201376,0.000616836,0.0006970523,0.0005675448,0.0007634261],"category_scores_gemma":[0.004974154,0.0001923023,0.0003187173,0.000525417,0.0002180224,0.0006375554,0.0006070875,0.000695146,0.0002191882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006626513,"about_ca_system_score_gemma":0.0005247001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01166106,"about_ca_topic_score_gemma":0.009144136,"domain_scores_codex":[0.9996598,0.00009710568,0.00002384857,0.000101078,0.00007480253,0.0000433391],"domain_scores_gemma":[0.9979298,0.001035254,0.0003160707,0.0001749868,0.0003905607,0.0001533344],"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":[0.0001323015,0.00007821204,0.05686615,0.00002478788,0.00003581031,0.00007102622,0.00003016721,0.9207428,0.001034928,0.0006102449,0.0005364057,0.0198372],"study_design_scores_gemma":[0.000002194279,0.00003593205,0.005103577,0.000003349742,0.000002423343,0.00001270028,0.00001333195,0.9940501,0.0002336143,0.000455641,0.0000827422,0.000004466121],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9012765,0.0001789282,0.09530524,0.0003251023,0.00003100248,0.00006008772,0.001366675,0.0004308369,0.0010255],"genre_scores_gemma":[0.9926797,0.00003433738,0.006205952,0.00001426503,0.000007763308,0.000023612,0.0008048365,0.00000845552,0.0002210216],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01166106,"threshold_uncertainty_score":0.02318633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02081335526796194,"score_gpt":0.2444084943999739,"score_spread":0.2235951391320119,"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."}}