{"id":"W2900796797","doi":"10.4095/304278","title":"Mer Bleue, Ontario, Arctic surrogate study site project, 2016: global navigation satellite system survey report","year":2017,"lang":"en","type":"report","venue":"","topic":"Offshore Engineering and Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada","funders":"","keywords":"Satellite; Arctic; Remote sensing; The arctic; Satellite system; Environmental science; Geography; Meteorology; Oceanography; Geology; Engineering; GNSS applications; Aerospace 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008173921,0.0004539558,0.0003265397,0.001016881,0.001803067,0.001280144,0.0006819037,0.0003040443,0.008111067],"category_scores_gemma":[0.00157825,0.0004465073,0.0001664827,0.003250966,0.0003324074,0.0004719111,0.0005573212,0.0005161382,0.00450706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01366006,"about_ca_system_score_gemma":0.04398915,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.980567,"about_ca_topic_score_gemma":0.9942328,"domain_scores_codex":[0.9991288,0.00003140823,0.00003215422,0.00006613694,0.0006561378,0.00008542153],"domain_scores_gemma":[0.9969659,0.00006877702,0.0001449108,0.0001158213,0.002474128,0.0002304732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002624899,0.00006973116,0.08163723,0.0003018674,0.00003523082,0.0002547789,0.0006936245,0.000887544,0.001313136,0.0007761492,0.8800634,0.03370478],"study_design_scores_gemma":[0.00007493165,0.00003679351,0.3589925,0.0001545514,0.00001461347,0.00007862481,0.001299325,0.0009588087,0.0008765642,0.000153975,0.6373314,0.00002786642],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.04493599,0.001704884,0.001641667,0.002833066,0.0004143283,0.0006733481,0.8441947,0.0004720134,0.10313],"genre_scores_gemma":[0.1166759,0.003370133,0.006451807,0.0005756183,0.00009684274,0.0006652916,0.6248419,0.0004155668,0.246907],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01943296,"threshold_uncertainty_score":0.09911114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04161791109957558,"score_gpt":0.2906730430778196,"score_spread":0.249055131978244,"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."}}