{"id":"W6892327132","doi":"10.5065/d6f47m5p","title":"SWL11 Bottle data. Version 1.0","year":2015,"lang":"en","type":"dataset","venue":"Earth Observing Laboratory","topic":"Reinforcement Learning in Robotics","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fisheries and Oceans Canada","funders":"","keywords":"Coast guard; Cruise; Hydrography; Water bottle; Longitude; Bottle; Latitude; Colored dissolved organic matter","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":[0.001104411,0.00381645,0.001775392,0.003136338,0.001000252,0.002078378,0.004199812,0.002430086,0.03465309],"category_scores_gemma":[0.003772723,0.0009007445,0.001913305,0.005490196,0.0005629969,0.00185626,0.002417132,0.002007121,0.08064358],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001479985,"about_ca_system_score_gemma":0.002709778,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03632735,"about_ca_topic_score_gemma":0.07062808,"domain_scores_codex":[0.998633,0.0001860677,0.0001818688,0.0004187135,0.0003565722,0.0002237677],"domain_scores_gemma":[0.9987364,0.0002349343,0.0001048536,0.0003689438,0.0004018901,0.0001528729],"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":[0.00007460247,0.00004678478,0.0009959615,0.0006029336,0.00003884165,0.00003099554,0.00002763144,0.0005305193,0.0002717159,0.0002540416,0.9939236,0.003202355],"study_design_scores_gemma":[0.0002943107,0.00005504489,0.005682333,0.0002178629,0.00004436546,0.0001158791,0.0001392454,0.001757089,0.001057398,0.001006219,0.9895635,0.00006668898],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003205634,0.00006458539,0.0001026193,0.00004653859,0.00003123157,0.00001895194,0.9979875,0.0009212492,0.0005066514],"genre_scores_gemma":[0.0002741082,0.00002757725,0.0002578475,0.00002542023,0.000003953856,0.00004827369,0.9989721,0.00005906256,0.0003317674],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03632735,"threshold_uncertainty_score":0.1159261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05144617681186247,"score_gpt":0.2781954881882576,"score_spread":0.2267493113763951,"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."}}