{"id":"W4311762460","doi":"10.1029/2022jc019539","title":"What's New at <i>JGR‐Oceans</i>? Confronting Bias, Burn Out, and Big Data","year":2022,"lang":"en","type":"article","venue":"Journal of Geophysical Research Oceans","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"CLARITY; Excellence; Public relations; Scope (computer science); Psychology; Data science; Political science; Computer science; Sociology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","scholarly_communication","open_science"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03168539,0.0001691793,0.0004718387,0.0007618206,0.001229395,0.002687947,0.004856778,0.00003309258,0.0005073676],"category_scores_gemma":[0.01062159,0.0001243814,0.0001540583,0.00142721,0.000335441,0.001580895,0.01132995,0.001148629,0.0001569163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000158772,"about_ca_system_score_gemma":0.0004810143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001455586,"about_ca_topic_score_gemma":0.00008246402,"domain_scores_codex":[0.9881284,0.001245136,0.001085152,0.0009502859,0.00785153,0.000739542],"domain_scores_gemma":[0.9909492,0.0049445,0.0005695702,0.002078944,0.0007812913,0.0006764653],"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":[0.0001131591,0.0001392406,0.000875557,0.000004001695,0.00003513745,0.0001379412,0.0008063521,0.00007224455,0.0002015393,0.0003542184,0.6683659,0.3288947],"study_design_scores_gemma":[0.0007739211,0.0005425742,0.004029112,0.00004536943,0.00002201665,0.00006851144,0.007421624,0.009960913,0.00003802849,0.01443006,0.9624962,0.0001717219],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9378384,0.002584187,0.008316866,0.03537929,0.01195939,0.0004890644,0.0002718885,0.00004483947,0.003116008],"genre_scores_gemma":[0.9213551,0.0004423328,0.0009467933,0.0005254628,0.003213417,0.000001467356,0.00004361973,0.00003175019,0.07344005],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.328723,"threshold_uncertainty_score":0.9983473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5430703023252462,"score_gpt":0.4788529656400736,"score_spread":0.0642173366851726,"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."}}