{"id":"W2159350238","doi":"10.1071/aseg2009ab048","title":"An Automated sparse constraint model builder for ubc-gif gravity and magnetic inversions","year":2009,"lang":"en","type":"article","venue":"ASEG Extended Abstracts","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Inversion (geology); Geology; Geophysics; Suite; Constraint (computer-aided design); Computer science; Seismology; Mathematics","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":[0.0002566382,0.0001834951,0.0002204854,0.00006335496,0.0001944152,0.00007052901,0.0001493314,0.0001111957,0.0001287565],"category_scores_gemma":[0.0001123824,0.0001484204,0.00006580251,0.0001672159,0.0001163985,0.0002122381,0.000004977095,0.0001744751,0.00003906132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003027463,"about_ca_system_score_gemma":0.00005847861,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002706735,"about_ca_topic_score_gemma":0.0001137908,"domain_scores_codex":[0.9987051,0.0000626692,0.0002284052,0.0003758266,0.000176278,0.0004517337],"domain_scores_gemma":[0.999029,0.0002213093,0.00006842915,0.000196383,0.00004728056,0.0004376259],"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":[0.00006971218,0.0001645885,0.0001032123,0.00001069816,0.000006796212,0.00001802052,0.00004653979,0.01005489,0.0006591494,0.0007125494,0.000653394,0.9875004],"study_design_scores_gemma":[0.0003471789,0.0005937837,0.5984783,0.000006005789,0.00002316249,0.00001216265,0.00001986643,0.3390085,0.0003915866,0.06077654,0.0001740814,0.0001689178],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956245,0.0001369699,0.001116847,0.000924635,0.00008486397,0.0003724142,0.0001671439,0.0002558186,0.001316749],"genre_scores_gemma":[0.9663637,0.00001389264,0.03244148,0.0009179784,0.00005531246,0.000001784519,0.0001035605,0.0000032302,0.00009901401],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9873315,"threshold_uncertainty_score":0.6052408,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02138410622338748,"score_gpt":0.2802381776584079,"score_spread":0.2588540714350204,"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."}}