{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0012904,0.0004485056,0.0004507198,0.0002138041,0.0002125641,0.0005279505,0.007758258,0.0004187763,0.0002130235],"category_scores_gemma":[0.000754981,0.0004667648,0.00005209352,0.0008700198,0.00007032823,0.001356996,0.005282939,0.0009480469,0.004799472],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009863755,"about_ca_system_score_gemma":0.001220714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001068431,"about_ca_topic_score_gemma":0.00005125677,"domain_scores_codex":[0.9964263,0.0002653869,0.0004717934,0.00103674,0.0012168,0.0005829415],"domain_scores_gemma":[0.9920359,0.0001554409,0.0004475366,0.006587293,0.0004632886,0.0003105507],"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":[0.000002792715,0.00002031862,0.00003178467,0.00009844673,0.00003415883,0.00006874364,0.00002533145,0.007094455,0.000008244554,0.00002788109,0.9924624,0.0001254305],"study_design_scores_gemma":[0.0002700934,0.00007780117,0.0001068813,0.0001375353,0.00003237542,0.000002703955,0.000009401089,0.01742897,0.00001577976,0.0000108525,0.9814116,0.0004959541],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001349771,0.0009352803,0.01612832,0.0001980385,0.004134513,0.000235856,0.9777297,0.0003798268,0.0002449747],"genre_scores_gemma":[0.000007653869,0.0001709245,0.0140566,0.0009528129,0.0005251975,0.000005715572,0.9835272,0.00003565312,0.0007182233],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01105076,"threshold_uncertainty_score":0.9997784,"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."}}