{"id":"W6962459317","doi":"10.1594/pangaea.882174","title":"Moving Vessel Profiling (MVP) data collected by the CCGS Amundsen in the Canadian Arctic","year":2017,"lang":"en","type":"other","venue":"Figshare","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Transect; Arctic; Profiling (computer programming); Water column; The arctic; Open water","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006386114,0.0004686943,0.0002871244,0.002601722,0.002268793,0.001138471,0.0007824014,0.0003417479,0.005665783],"category_scores_gemma":[0.001122387,0.0002614686,0.0002972959,0.005851456,0.0004141104,0.000369698,0.0008014582,0.0004034179,0.001533959],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009191922,"about_ca_system_score_gemma":0.01983532,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9887455,"about_ca_topic_score_gemma":0.9956303,"domain_scores_codex":[0.9990429,0.00003161425,0.00002397446,0.0001438742,0.0005799011,0.0001778016],"domain_scores_gemma":[0.9989605,0.00003242116,0.00005096039,0.00005158651,0.0007664895,0.0001380865],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006509998,0.0001241599,0.4213457,0.0007700567,0.000296318,0.0009659795,0.006003162,0.0155738,0.01911533,0.004471328,0.3066284,0.2240547],"study_design_scores_gemma":[0.00002968487,0.00003899521,0.6939734,0.0002518664,0.00005914102,0.0001049455,0.002629813,0.00487073,0.002900353,0.0003131608,0.2947305,0.00009737938],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.4719058,0.001419316,0.005284735,0.0009094711,0.0002924342,0.0002915437,0.3999639,0.001786295,0.1181466],"genre_scores_gemma":[0.6800627,0.001548507,0.02174198,0.0003903191,0.00006799152,0.000230211,0.2545313,0.0009976686,0.04042941],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.01125449,"threshold_uncertainty_score":0.06669235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1117895290679397,"score_gpt":0.3136647874609152,"score_spread":0.2018752583929755,"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."}}