{"id":"W4251345933","doi":"10.4095/314595","title":"Mer Bleue, Ontario, Arctic surrogate study site project, 2018 update: global navigation satellite system survey report","year":2019,"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; Satellite system; The arctic; Remote sensing; Arctic; Geography; Environmental science; Meteorology; Geology; Oceanography; 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":[0.001567545,0.0006607717,0.0004839277,0.001839996,0.001267355,0.001333544,0.001105696,0.0004161395,0.01386391],"category_scores_gemma":[0.003717424,0.0005125654,0.0002435817,0.005573117,0.0002889231,0.0006921346,0.0006634624,0.0006343688,0.01029204],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01075815,"about_ca_system_score_gemma":0.03745751,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9618512,"about_ca_topic_score_gemma":0.9828866,"domain_scores_codex":[0.9987073,0.00004488378,0.00007821214,0.00009227612,0.000966873,0.0001105104],"domain_scores_gemma":[0.9917191,0.000174878,0.0003517012,0.0002900601,0.007051615,0.0004126165],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.0001020137,0.00003040133,0.02293661,0.0001853641,0.00001395631,0.00005929358,0.0001616292,0.0003037115,0.0002291482,0.0002183401,0.9542285,0.02153112],"study_design_scores_gemma":[0.00005250246,0.00002200992,0.1555568,0.0001629915,0.00001528289,0.00003973766,0.0004285962,0.0003547616,0.0003306727,0.0000940913,0.8429242,0.00001841078],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.009189554,0.001055444,0.0008710101,0.001230067,0.0004271121,0.0003339588,0.952869,0.0004138787,0.03360996],"genre_scores_gemma":[0.02267617,0.002317259,0.003891942,0.0003619459,0.00009808502,0.0007032285,0.868822,0.0003226203,0.1008068],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03814882,"threshold_uncertainty_score":0.07805616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03031130799435564,"score_gpt":0.2681571166114619,"score_spread":0.2378458086171063,"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."}}