{"id":"W2134181566","doi":"10.1190/1.1444891","title":"Multisite, multifrequency tensor decomposition of magnetotelluric data","year":2001,"lang":"en","type":"article","venue":"Geophysics","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":444,"is_retracted":false,"has_abstract":true,"ca_institutions":"Geological Survey of Canada; Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Magnetotellurics; Distortion (music); Hessian matrix; Set (abstract data type); Data set; Range (aeronautics); Scale (ratio); Tensor (intrinsic definition); Computer science; Algorithm; Geology; Mathematics; Applied mathematics; Artificial intelligence; Geometry; Telecommunications; Physics; Engineering; Electrical engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001457685,0.0001433822,0.0002315454,0.00004973664,0.00008552286,0.00002019536,0.0004843196,0.00006104479,0.0007152699],"category_scores_gemma":[0.00006384459,0.0001168365,0.00006258186,0.0006218302,0.0000717857,0.000290438,0.00003754226,0.0001482251,0.0006182836],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002041655,"about_ca_system_score_gemma":0.00001547801,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004071846,"about_ca_topic_score_gemma":0.0002172029,"domain_scores_codex":[0.9987387,0.00009534645,0.0002440902,0.0003474214,0.0002540025,0.0003204508],"domain_scores_gemma":[0.9988823,0.0002721335,0.0001006512,0.0005596756,0.00006822568,0.0001170199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00004104416,0.00009685487,0.03504483,0.0000184575,0.00001437966,0.00001791353,0.00003042248,0.0003721272,0.001295723,0.00008858332,0.0001037342,0.962876],"study_design_scores_gemma":[0.0002963475,0.0002540945,0.9374024,0.00001177477,0.00003465809,0.00001337033,0.00001290745,0.04702282,0.0006480478,0.01153251,0.002548526,0.0002225628],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9904432,0.0003162384,0.004499075,0.0001232433,0.0002047259,0.000161462,0.0001888381,0.00005340817,0.00400985],"genre_scores_gemma":[0.9817742,0.000089567,0.01683936,0.0001216644,0.0002372109,6.234982e-7,0.0004670934,0.000004198532,0.0004661506],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9626534,"threshold_uncertainty_score":0.7946991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03651397790646969,"score_gpt":0.2860877066999749,"score_spread":0.2495737287935052,"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."}}