{"id":"W1977764052","doi":"10.1016/j.cageo.2006.12.007","title":"An artificial neural net assisted approach to editing edges in petrographic images collected with the rotating polarizer stage","year":2007,"lang":"en","type":"article","venue":"Computers & Geosciences","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brock University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Artificial neural network; Petrography; Pattern recognition (psychology); Stage (stratigraphy); Segmentation; Net (polyhedron); Enhanced Data Rates for GSM Evolution; Texture (cosmology); Computer vision; Image (mathematics); Geology; Mathematics; Geometry; Mineralogy","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":[0.0003962305,0.0004594548,0.0003245377,0.000860566,0.0003495729,0.0007283589,0.0007957629,0.0006819753,0.002042061],"category_scores_gemma":[0.0008536039,0.0003520932,0.000392359,0.000668695,0.000283472,0.0004905292,0.0003272849,0.0006074738,0.0004544794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003654233,"about_ca_system_score_gemma":0.0005645227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006282039,"about_ca_topic_score_gemma":0.01286713,"domain_scores_codex":[0.9998778,0.00001839919,0.000007197409,0.00002898338,0.00005090115,0.00001672889],"domain_scores_gemma":[0.9996752,0.0001058385,0.00002980179,0.00003194915,0.0001417229,0.00001556679],"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":[0.0003824712,0.0001014406,0.001075439,0.0001248605,0.00006043039,0.0001380221,0.0001086238,0.1003488,0.1495926,0.002644874,0.001723672,0.7436987],"study_design_scores_gemma":[0.000008701211,0.00004011411,0.001128455,0.00000572109,0.00002366904,0.00008400091,0.00002539918,0.9483243,0.04808891,0.0007172963,0.001539803,0.00001355706],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04612703,0.0001358226,0.9503406,0.00009998405,0.00006427222,0.00007085686,0.00008773468,0.00145104,0.001622726],"genre_scores_gemma":[0.1716002,0.0001708324,0.824016,0.0000510046,0.00003469244,0.00004090323,0.0001374949,0.0001728896,0.003775958],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006282039,"threshold_uncertainty_score":0.01249093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02442917192485867,"score_gpt":0.2848533985644492,"score_spread":0.2604242266395906,"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."}}