{"id":"W2940044694","doi":"10.5821/iwp.2018.20.14849","title":"Deep sea spy: a collaborative annotation tool","year":2018,"lang":"en","type":"article","venue":"Instrumentation viewpoint","topic":"Time Series Analysis and Forecasting","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Annotation; Oceanography; Geography; Computer science; Geology; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002236773,0.0001168041,0.0001375007,0.0001027728,0.0002475145,0.0002460245,0.0002507048,0.0000341911,0.0002030447],"category_scores_gemma":[0.00004198601,0.0001076664,0.00005266698,0.0008870351,0.00006742928,0.001011915,0.00009264434,0.0000571913,0.0003346309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007477964,"about_ca_system_score_gemma":0.00005724689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003484726,"about_ca_topic_score_gemma":0.00008593738,"domain_scores_codex":[0.9988636,0.00006946037,0.0003222656,0.0002980175,0.0002584651,0.0001882166],"domain_scores_gemma":[0.9991454,0.00002428873,0.0002003256,0.0002565405,0.0003198524,0.0000535292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002053975,0.00005416459,0.001272,0.00001366965,0.00006320089,0.000004881561,0.006811662,0.0001959998,0.001561929,0.09244926,0.001053181,0.8964995],"study_design_scores_gemma":[0.001338451,0.000830517,0.01735997,0.00006969536,0.00004762221,0.00003065701,0.002012654,0.895096,0.02662961,0.01512855,0.0407901,0.0006661596],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1356213,0.00007356827,0.8541714,0.002732418,0.0005061768,0.0002981892,0.000005538413,0.0001663623,0.006425034],"genre_scores_gemma":[0.8736989,0.00002087401,0.1246654,0.001246548,0.0001717009,0.00002980483,0.00002370533,0.000008729105,0.0001342724],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8958334,"threshold_uncertainty_score":0.4390509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01146442124517238,"score_gpt":0.2534684713405156,"score_spread":0.2420040500953432,"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."}}