{"id":"W3011374674","doi":"10.1177/0278364920908922","title":"The Canadian Planetary Emulation Terrain Energy-Aware Rover Navigation Dataset","year":2020,"lang":"en","type":"article","venue":"The International Journal of Robotics Research","topic":"Planetary Science and Exploration","field":"Physics and Astronomy","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Inertial measurement unit; Terrain; Global Positioning System; Mars Exploration Program; Remote sensing; Robot; Artificial intelligence; Real-time computing; Geography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0003960648,0.001507025,0.0007788234,0.002606674,0.001559622,0.001069448,0.002557156,0.001149678,0.007755984],"category_scores_gemma":[0.002375684,0.0003016899,0.0007143919,0.005334917,0.0005224215,0.0006396862,0.001204936,0.001171617,0.009665874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004019541,"about_ca_system_score_gemma":0.006791061,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7778875,"about_ca_topic_score_gemma":0.9240031,"domain_scores_codex":[0.9992405,0.00005236961,0.00003660963,0.0001712915,0.0003185501,0.0001807629],"domain_scores_gemma":[0.9985054,0.00009321446,0.00005907265,0.0002909483,0.00090842,0.000143077],"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":[0.0002223069,0.0001076467,0.01411271,0.0005855512,0.0001317781,0.000233654,0.0001522139,0.004810694,0.001541237,0.001056857,0.9481903,0.02885499],"study_design_scores_gemma":[0.0001430193,0.00005653785,0.0608936,0.0003188423,0.00007105739,0.0002249119,0.0006439407,0.01103325,0.003077418,0.0009442586,0.9224726,0.0001204417],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.007054692,0.0003145858,0.0006869292,0.0001261752,0.00006060651,0.00006659435,0.9854049,0.001522013,0.004763398],"genre_scores_gemma":[0.00632442,0.00009353199,0.001464633,0.00003276354,0.000005164335,0.00004040262,0.9909928,0.00007588427,0.0009705242],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2221125,"threshold_uncertainty_score":0.4468412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08275221228808084,"score_gpt":0.3447958770203047,"score_spread":0.2620436647322238,"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."}}