{"id":"W4296023017","doi":"10.3389/esss.2022.10051","title":"Reproducibility in Subsurface Geoscience","year":2022,"lang":"en","type":"article","venue":"Earth Science Systems and Society","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agile Scientific (Canada)","funders":"Imperial College London","keywords":"Reproducibility; Earth science; Government (linguistics); Confidentiality; Data science; Computer science; Environmental resource management; Environmental science; Geology; Chemistry","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","sts","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.09777931,0.0001075259,0.0002259504,0.0001972089,0.001960798,0.001135688,0.001744584,0.00001737653,0.00009249061],"category_scores_gemma":[0.00274066,0.00008469385,0.00007522098,0.005333835,0.0009255296,0.0006657194,0.002110767,0.0002126377,0.00002752465],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007912848,"about_ca_system_score_gemma":0.0002548213,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006993922,"about_ca_topic_score_gemma":0.00003293179,"domain_scores_codex":[0.9919323,0.000289014,0.0006177163,0.003235956,0.003403891,0.0005210959],"domain_scores_gemma":[0.9961451,0.0002994237,0.0001951065,0.003033159,0.0001676897,0.0001595481],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002533351,0.0006741201,0.6934807,0.00005440094,0.00001239086,0.00003725207,0.05057954,0.08702668,0.006881642,0.03719322,0.05124991,0.07278483],"study_design_scores_gemma":[0.0003272369,0.00009929181,0.5539066,0.0000112902,0.00000216579,0.00002923254,0.03913788,0.2968843,0.00006028081,0.001746656,0.1074879,0.0003070499],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919173,0.0002299765,0.001753143,0.0004873199,0.002526506,0.0003140896,0.00001928393,0.00004232158,0.002710094],"genre_scores_gemma":[0.9931384,0.000004787412,0.000897639,0.0001335134,0.00003222372,0.00001462633,8.885488e-7,0.000002692707,0.005775257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2098577,"threshold_uncertainty_score":0.9999012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09993759163541668,"score_gpt":0.3588518009897728,"score_spread":0.2589142093543562,"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."}}