{"id":"W2982694885","doi":"10.4236/ijg.2019.1010054","title":"Seismic Data Quality Control and Interpolation Using Principal Component Analysis","year":2019,"lang":"en","type":"article","venue":"International Journal of Geosciences","topic":"Seismic Imaging and Inversion Techniques","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada; Natural Resources Canada","funders":"Natural Resources Canada","keywords":"Principal component analysis; Interpolation (computer graphics); Quality (philosophy); Geology; Component (thermodynamics); Control (management); Data quality; Computer science; Statistics; Mathematics; Engineering; Artificial intelligence; Operations management","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004555999,0.001592187,0.0009474241,0.004411043,0.0009147248,0.001980302,0.001381585,0.0006835607,0.003108406],"category_scores_gemma":[0.01340675,0.0005727373,0.00121838,0.005160429,0.0007512942,0.001765373,0.001662843,0.00168914,0.001978492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006528337,"about_ca_system_score_gemma":0.002526573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005455118,"about_ca_topic_score_gemma":0.005257061,"domain_scores_codex":[0.9966837,0.0005070166,0.0003188544,0.0006594414,0.001651715,0.0001792578],"domain_scores_gemma":[0.994619,0.0009121014,0.0004281327,0.001056738,0.002902053,0.00008186678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003190516,0.0001347975,0.006188136,0.0003363954,0.0001075627,0.0001372074,0.0003925593,0.05167711,0.03507223,0.007326019,0.005749271,0.8925596],"study_design_scores_gemma":[0.00005331317,0.0001603052,0.01211836,0.00006619699,0.00008549209,0.0001922552,0.0002244264,0.8914886,0.05991144,0.00898495,0.02655491,0.0001597268],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007812643,0.00009414361,0.9873735,0.00006199305,0.00004228075,0.00009443676,0.0002591465,0.003720066,0.0005417014],"genre_scores_gemma":[0.07167131,0.0002346332,0.9246553,0.00003025864,0.00004158774,0.0001992683,0.001510344,0.0007831876,0.0008740603],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005455118,"threshold_uncertainty_score":0.0240947,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04167339695103327,"score_gpt":0.3145678568655959,"score_spread":0.2728944599145626,"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."}}