{"id":"W3034529268","doi":"10.3390/ijgi9060383","title":"Earth Observation and Artificial Intelligence for Improving Safety to Navigation in Canada Low-Impact Shipping Corridors","year":2020,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Arctic and Russian Policy Studies","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Hydrographic Service","funders":"","keywords":"Government (linguistics); Agency (philosophy); Service (business); Transport engineering; Business; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005301599,0.00007763108,0.0001298057,0.0001257397,0.0001856593,0.0001153042,0.0001876691,0.00003575559,0.00001582504],"category_scores_gemma":[0.00139162,0.00007139393,0.00004518221,0.0001801656,0.0000334415,0.001757358,0.00003517147,0.0001220463,0.000003896172],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000603339,"about_ca_system_score_gemma":0.0009373745,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.2649226,"about_ca_topic_score_gemma":0.2070346,"domain_scores_codex":[0.9986337,0.00003662851,0.00062915,0.00005661508,0.0004844269,0.0001594536],"domain_scores_gemma":[0.9987477,0.0001467756,0.0004176575,0.00002776995,0.0005289278,0.0001311494],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001322757,0.00002550636,0.02873735,0.0001014771,0.00012785,0.000008341564,0.1194718,0.0343068,0.0001908024,0.02390296,0.001682459,0.7901219],"study_design_scores_gemma":[0.002309451,0.0009775977,0.4953697,0.001178261,0.0001043207,0.00005056011,0.1930978,0.2081227,0.002279886,0.01515773,0.08003141,0.001320635],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7829343,0.00002773251,0.160204,0.05441796,0.001279047,0.0004683459,0.00008650139,0.00001278438,0.0005692692],"genre_scores_gemma":[0.9962525,0.0000279324,0.001583755,0.001742582,0.0003675008,0.000003422878,0.00001519936,0.000002857292,0.000004230003],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7888013,"threshold_uncertainty_score":0.807435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03270232567105162,"score_gpt":0.3169494822427248,"score_spread":0.2842471565716732,"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."}}