{"id":"W2979662483","doi":"10.1016/j.rse.2019.111412","title":"Automating offshore infrastructure extractions using synthetic aperture radar &amp; Google Earth Engine","year":2019,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Oil Spill Detection and Mitigation","field":"Environmental Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Nippon Foundation; University of British Columbia","keywords":"Environmental science; Synthetic aperture radar; Offshore wind power; Submarine pipeline; Remote sensing; Earth observation; Wind power; Environmental resource management; Meteorology; Computer science; Geology; Oceanography; Geography; Satellite","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001478426,0.0001875718,0.0001979753,0.00005346003,0.0001308942,0.00001713179,0.00007783969,0.0001191713,0.002202037],"category_scores_gemma":[0.00004043395,0.0001828236,0.00009584803,0.0001281499,0.0001156172,0.0001387305,0.00008747599,0.0002123559,0.0005348366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002405684,"about_ca_system_score_gemma":0.000008881666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001556307,"about_ca_topic_score_gemma":0.00002402772,"domain_scores_codex":[0.9986862,0.00006100618,0.0002891926,0.0003369956,0.0003799595,0.0002466547],"domain_scores_gemma":[0.9992602,0.00006066103,0.0001890614,0.0003940461,0.000003975051,0.00009208175],"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":[0.000005995616,0.00001933231,0.0007992266,0.00002010646,0.00001508773,0.000001932749,0.0002075578,0.04923811,0.8150573,0.000002772635,0.00004439195,0.1345881],"study_design_scores_gemma":[0.001082305,0.0001825911,0.1734928,0.0003958329,0.0001458177,0.0004215962,0.0004158069,0.5833768,0.1104377,0.0003525185,0.1286373,0.001058876],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9754139,0.00004284689,0.02069403,0.0001195989,0.0002163501,0.0002403507,0.000005361865,0.00005857133,0.003208969],"genre_scores_gemma":[0.8793615,0.00002537129,0.1195453,0.00009594877,0.00003632137,6.479053e-8,0.00001146419,0.00002918584,0.0008949016],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7046196,"threshold_uncertainty_score":0.9987101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007666002356057106,"score_gpt":0.2073524715422289,"score_spread":0.1996864691861718,"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."}}