{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002113988,0.000237752,0.0003185409,0.0002415654,0.0003405718,0.0002946797,0.0005716812,0.0004533108,0.001296537],"category_scores_gemma":[0.0003014915,0.0002036542,0.0001703416,0.0002055181,0.000315544,0.0006919766,0.0005610724,0.0003864642,0.0002677934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001878578,"about_ca_system_score_gemma":0.0003578605,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009280063,"about_ca_topic_score_gemma":0.001376636,"domain_scores_codex":[0.9998249,0.00002445936,0.000006482656,0.00003364725,0.00009480344,0.00001577538],"domain_scores_gemma":[0.9998267,0.00004154456,0.00003185647,0.00002467658,0.00004676092,0.00002853993],"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":[0.0002040052,0.0001615559,0.004338067,0.0001027241,0.00002345795,0.0003156114,0.0002913761,0.01023279,0.9096895,0.001139871,0.000675312,0.07282586],"study_design_scores_gemma":[0.00009319604,0.00314059,0.02226502,0.00004386136,0.0001114185,0.001165841,0.0005642788,0.2599497,0.6884218,0.001793327,0.02232846,0.0001224612],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7602452,0.0002592665,0.2306177,0.0003429235,0.00006507411,0.0002915789,0.00016633,0.00169628,0.006315578],"genre_scores_gemma":[0.7424241,0.0002046277,0.2519159,0.0001061576,0.000019614,0.0001605296,0.0001221447,0.0000399189,0.005007055],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001296537,"threshold_uncertainty_score":0.00433737,"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."}}