{"id":"W2969837149","doi":"10.3390/jmse7090287","title":"The Maturity of Automatic Identification Systems (AIS) and Its Implications for Innovation","year":2019,"lang":"en","type":"article","venue":"Journal of Marine Science and Engineering","topic":"Maritime Navigation and Safety","field":"Engineering","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. Fleet Forces Command; Maritime Administration; Institute of Musculoskeletal Health and Arthritis; U.S. Department of Transportation","keywords":"Automatic Identification System; Maturity (psychological); Identification (biology); Submarine pipeline; Maritime industry; Business; Computer science; Process management; Engineering; Computer security; Political science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001047424,0.00004419365,0.00008748615,0.00014225,0.00006129618,0.00006543275,0.00009852436,0.00001896728,0.000001904843],"category_scores_gemma":[0.0001860863,0.00003368037,0.00001165342,0.0003871444,0.00002120197,0.0002963359,0.00002680753,0.00006370087,3.648104e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002715818,"about_ca_system_score_gemma":0.0000236416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.902238e-7,"about_ca_topic_score_gemma":1.997207e-7,"domain_scores_codex":[0.9994237,0.000002941559,0.0003193336,0.00004623081,0.0001266679,0.00008108804],"domain_scores_gemma":[0.9993502,0.0000843622,0.00009890508,0.00007066335,0.0003681509,0.00002774833],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001145712,0.00001838485,0.005353949,0.00163284,0.00005005809,2.026279e-7,0.000290354,0.05377905,0.7238065,0.1266774,0.0003025964,0.08807717],"study_design_scores_gemma":[0.0001992723,0.00002505356,0.1821403,0.00005952317,0.000009283947,0.00002965644,0.00004736468,0.8131143,0.002973161,0.0004020746,0.0009378782,0.00006205867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950534,0.000241177,0.003725114,0.0001864155,0.000360452,0.0001679585,0.000002625421,0.00001785375,0.0002449899],"genre_scores_gemma":[0.9993829,0.0001148565,0.0004350033,0.00000306187,0.00002677227,0.000004189989,7.373005e-7,0.000004243708,0.00002824897],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7593353,"threshold_uncertainty_score":0.1373446,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009211074922805281,"score_gpt":0.2351084513130262,"score_spread":0.2258973763902209,"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."}}