{"id":"W4403917947","doi":"10.1126/scirobotics.adn7299","title":"Reinforcement learning–based framework for whale rendezvous via autonomous sensing robots","year":2024,"lang":"en","type":"article","venue":"Science Robotics","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"","keywords":"Reinforcement learning; Whale; Rendezvous; Computer science; Robot; Reinforcement; Artificial intelligence; Human–computer interaction; Engineering; Psychology; Aerospace engineering; Ecology; Biology; Social psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.0004088893,0.0001324476,0.0001303296,0.000148251,0.00027992,0.0003909,0.0003324063,0.00007053243,0.000008190666],"category_scores_gemma":[0.00001737151,0.0001273572,0.00006270793,0.0005319511,0.0001069406,0.0002050253,0.00005866668,0.0002335864,0.00005415331],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000221755,"about_ca_system_score_gemma":0.0001068825,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001182256,"about_ca_topic_score_gemma":0.000005805934,"domain_scores_codex":[0.998856,0.00001529716,0.0002520203,0.0002270502,0.000267673,0.0003819784],"domain_scores_gemma":[0.9993021,0.0001210572,0.00002777285,0.000370036,0.00007717717,0.0001017868],"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":[9.787425e-7,0.000003274604,0.000008255271,0.00006749322,0.000007652105,0.00000181838,0.0003829811,0.973486,0.006050764,0.001628849,0.00003527948,0.01832664],"study_design_scores_gemma":[0.00005809006,0.00004167046,0.000007943238,0.0001458291,0.00001027645,0.000008127859,0.0001438622,0.9806188,0.01066564,0.001346423,0.006784077,0.0001692405],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0009262774,0.0003489064,0.996309,0.000475797,0.0005528589,0.0001957716,6.909626e-7,0.0006170088,0.0005736868],"genre_scores_gemma":[0.8443041,0.00001834589,0.15527,0.00006292177,0.00007261301,0.000007671612,0.000004391491,0.00002783548,0.0002320029],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8433779,"threshold_uncertainty_score":0.5193475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02347425021307041,"score_gpt":0.2672314770668685,"score_spread":0.243757226853798,"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."}}