{"id":"W4404135735","doi":"10.1016/j.cageo.2024.105768","title":"Curvilinear lineament extraction: Bayesian optimization of Principal Component Wavelet Analysis and Hysteresis Thresholding","year":2024,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Image and Signal Denoising Methods","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec en Abitibi-Témiscamingue","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies","keywords":"Principal component analysis; Wavelet; Thresholding; Curvilinear coordinates; Lineament; Artificial intelligence; Pattern recognition (psychology); Geology; Computer science; Bayesian probability; Component (thermodynamics); Wavelet transform; Extraction (chemistry); Data mining; Mathematics; Seismology; Image (mathematics); Tectonics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001003052,0.0001625252,0.0002874929,0.0005911011,0.000197597,0.0006730993,0.0006155893,0.0000464243,0.00001162704],"category_scores_gemma":[0.00002224499,0.0001363453,0.0001294892,0.001936756,0.000165023,0.0007761893,0.0002934168,0.0001120162,0.000002098355],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003462617,"about_ca_system_score_gemma":0.00006796743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001058823,"about_ca_topic_score_gemma":0.000006168912,"domain_scores_codex":[0.9981396,0.0001477285,0.0003828066,0.0006085287,0.0004663171,0.000255006],"domain_scores_gemma":[0.9990681,0.0002872729,0.0001127346,0.0003392502,0.00008420832,0.0001083723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003032257,0.0002575706,0.003355734,0.0003534674,0.0005791492,0.0001933798,0.004945244,0.4401639,0.00720695,0.01428663,0.0003075385,0.5283201],"study_design_scores_gemma":[0.0001076462,0.00007802358,0.002457348,0.00007825429,0.0000740623,0.00002068392,0.00003204855,0.9936722,0.002500513,0.0002339753,0.0005905791,0.0001546616],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03128289,0.0007688301,0.9661548,0.0005504741,0.0008727004,0.0001075092,0.000002792206,0.0001219527,0.000138023],"genre_scores_gemma":[0.5052066,0.00005778898,0.4945384,0.00007818253,0.00006454432,0.000002790046,0.000003056491,0.000004077988,0.00004454098],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5535083,"threshold_uncertainty_score":0.6490712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02217258241098983,"score_gpt":0.2982061213094453,"score_spread":0.2760335388984555,"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."}}