{"id":"W1984469841","doi":"10.1109/crv.2014.22","title":"Exploring Underwater Environments with Curiosity","year":2014,"lang":"en","type":"article","venue":"","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Curiosity; Variety (cybernetics); Human–computer interaction; Robot; Mobile robot; Semantics (computer science); Sonar; Plan (archaeology); Motion planning; Underwater; Artificial intelligence; Geography","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00005613624,0.00007233924,0.00006887437,0.00002018123,0.00003936564,0.00002577699,0.0001133038,0.00001495474,0.00003769838],"category_scores_gemma":[1.068644e-7,0.00005278308,0.00001357674,0.0000301724,0.00001167693,0.0001632409,0.00002601543,0.00005203144,0.0002029449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002531714,"about_ca_system_score_gemma":7.338696e-7,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001546826,"about_ca_topic_score_gemma":0.00001360017,"domain_scores_codex":[0.9996302,0.00001577824,0.00008413255,0.00007094045,0.00008191585,0.0001170286],"domain_scores_gemma":[0.9996857,0.00001034003,0.000007247131,0.0002529848,0.000002526643,0.00004121081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007093471,0.0004036468,0.08499283,0.0004829228,0.0008859012,0.00001007789,0.01211848,0.1066847,0.5180624,0.01304987,0.001301044,0.2619372],"study_design_scores_gemma":[0.0007253758,0.00007807723,0.01190873,0.00004462348,0.00001484983,0.00001216129,0.000347798,0.02153815,0.2838574,0.0003261704,0.6806546,0.0004921043],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.585879,0.00001762925,0.3891422,0.00009293756,0.00002989143,0.00006575917,2.835835e-7,0.0002638051,0.02450851],"genre_scores_gemma":[0.997438,0.000034835,0.001948451,0.00005070794,0.00002516496,0.00002359182,0.000001830723,0.00001754365,0.000459853],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6793535,"threshold_uncertainty_score":0.2608513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05371804485144225,"score_gpt":0.1807024290317797,"score_spread":0.1269843841803374,"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."}}