{"id":"W2042758054","doi":"10.1190/1.3679324","title":"An automatic network-extraction algorithm applied to magnetic survey data for the identification and extraction of geologic lineaments","year":2012,"lang":"en","type":"article","venue":"The Leading Edge","topic":"Geophysical and Geoelectrical Methods","field":"Earth and Planetary Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Natural Resources Canada","funders":"","keywords":"Lineament; Geology; Magnetic survey; Algorithm; Magnetic anomaly; Geophysics; Data mining; Computer science; Seismology; Tectonics","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":[0.002492712,0.00007487971,0.0001127395,0.00002167621,0.0002028868,0.00004318815,0.0002870109,0.00003881181,0.00004915433],"category_scores_gemma":[0.0001846868,0.00004130831,0.00001432637,0.0002383211,0.00004468677,0.0001894311,0.0000165318,0.00009603985,0.00007135847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002412388,"about_ca_system_score_gemma":0.00000622578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004411204,"about_ca_topic_score_gemma":0.0001416416,"domain_scores_codex":[0.9991024,0.000208844,0.0001841891,0.0001600161,0.0001135652,0.0002309881],"domain_scores_gemma":[0.9978455,0.001615787,0.0001037985,0.0003460828,0.00002496082,0.00006383708],"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":[0.00001968866,0.00002641756,0.006060899,0.000008504603,0.000007710893,2.27833e-8,0.00008865495,0.000952336,0.0006666944,0.00001763434,0.000411488,0.9917399],"study_design_scores_gemma":[0.00005258468,0.00008810542,0.7473426,0.000002642246,0.00003372773,0.000002009685,0.00002279123,0.2503333,0.0001364364,0.001164102,0.0007698973,0.00005179936],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6891475,0.001215369,0.3060908,0.0005323103,0.00135026,0.001231244,0.0001165482,0.00006722186,0.0002486898],"genre_scores_gemma":[0.9904557,0.00002300291,0.00869414,0.00009740343,0.0003974463,0.000007536658,0.000166934,0.000002683954,0.0001551077],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9916881,"threshold_uncertainty_score":0.1684504,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07866675364362721,"score_gpt":0.3402954975210614,"score_spread":0.2616287438774342,"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."}}