{"id":"W2103643366","doi":"10.1109/crv.2011.27","title":"Monitoring Underwater Sensors with an Amphibious Robot","year":2011,"lang":"en","type":"article","venue":"","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Underwater; Computer science; Robot; Marine engineering; Artificial intelligence; Engineering; Geology; Oceanography","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":[0.00004966775,0.0001058107,0.00009130139,0.00003893678,0.00004644554,0.00003622945,0.000167039,0.00004049348,0.00006859525],"category_scores_gemma":[7.844731e-8,0.00007694004,0.00001761098,0.00006481205,0.00001724697,0.0001678029,0.0000198874,0.00007055249,0.00009553503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000229317,"about_ca_system_score_gemma":0.000002987486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001577807,"about_ca_topic_score_gemma":0.00005419016,"domain_scores_codex":[0.9995066,0.00001894598,0.0001247187,0.00009999122,0.00008074322,0.0001690037],"domain_scores_gemma":[0.9995198,0.000005107045,0.00001157457,0.000362973,0.00002421485,0.0000763112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002677377,0.0007544494,0.2953014,0.0004599637,0.001220957,0.0001457152,0.06436985,0.04288063,0.4933022,0.002329677,0.0002636319,0.09870377],"study_design_scores_gemma":[0.0006328053,0.0002432629,0.01540001,0.00007584791,0.0000234356,0.00007705645,0.004169504,0.007977337,0.9668454,0.0003461861,0.003546236,0.0006629159],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9391328,0.00005505466,0.03594231,0.00001432902,0.0000560789,0.00009137435,4.472921e-7,0.0007350591,0.02397259],"genre_scores_gemma":[0.9816911,0.00001884638,0.01772955,0.000009594919,0.0000466362,0.000009801382,0.000001186381,0.00003338117,0.0004599227],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4735432,"threshold_uncertainty_score":0.3137524,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04863347522019366,"score_gpt":0.2149161116645084,"score_spread":0.1662826364443148,"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."}}