{"id":"W4234035343","doi":"10.1002/essoar.10507251.1","title":"Exploration of data space through trans-dimensional sampling: A case study of 4D seismics","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Seismology and Earthquake Studies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Space (punctuation); Sampling (signal processing); Computer science; Space Science; World Wide Web; Information retrieval; Astronomy; Physics; Telecommunications","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":[],"consensus_categories":[],"category_scores_codex":[0.0004244086,0.0002178685,0.0005330636,0.00007368083,0.0001289447,0.00004672934,0.000988932,0.0001652699,0.00001394937],"category_scores_gemma":[0.00005185974,0.0001994869,0.00007303812,0.000234631,0.0001160481,0.0007442799,0.003145003,0.000328139,8.555102e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009569443,"about_ca_system_score_gemma":0.0002823755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00393919,"about_ca_topic_score_gemma":0.001085697,"domain_scores_codex":[0.9980043,0.0001873635,0.0005048544,0.0008177232,0.0003214304,0.0001643603],"domain_scores_gemma":[0.9971644,0.000245538,0.0002623691,0.002003317,0.0002939579,0.00003038599],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002039422,0.01047579,0.005346408,0.00151722,0.00491159,0.007865784,0.488317,0.3955184,0.0003151796,0.03865148,0.003202043,0.04367518],"study_design_scores_gemma":[0.007858021,0.003390768,0.004207982,0.00105722,0.001460223,0.005726604,0.2741786,0.6305121,0.005438625,0.05914082,0.003374089,0.003654947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4009899,0.0003211954,0.5971004,0.0008202937,0.0003901779,0.000249749,0.00001973366,0.00003429846,0.00007424515],"genre_scores_gemma":[0.7931919,0.00005217028,0.2064829,0.0001315925,0.00003120893,0.00001098429,0.00005519246,0.000006488272,0.00003762331],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3922019,"threshold_uncertainty_score":0.8134841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2880744303064081,"score_gpt":0.3715966185824559,"score_spread":0.08352218827604774,"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."}}