{"id":"W2346479336","doi":"","title":"Edge detection of petrographic images using genetic programming","year":2000,"lang":"en","type":"article","venue":"","topic":"Evolutionary Algorithms and Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"","keywords":"Edge detection; Detector; Computer vision; Genetic programming; Computer science; Artificial intelligence; Petrography; Canny edge detector; Image processing; Enhanced Data Rates for GSM Evolution; Sampling (signal processing); Pattern recognition (psychology); Image (mathematics); Mineralogy; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001183766,0.0007685368,0.0008704317,0.0008827506,0.0004069267,0.0008498288,0.001076879,0.0009326209,0.001134465],"category_scores_gemma":[0.00259373,0.0004163746,0.0007556201,0.0006071245,0.0008660021,0.00059844,0.000606033,0.0009633932,0.0001699586],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008986588,"about_ca_system_score_gemma":0.0007340505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004212184,"about_ca_topic_score_gemma":0.003382155,"domain_scores_codex":[0.9996182,0.0001253623,0.00001954701,0.00008507354,0.000109834,0.00004200231],"domain_scores_gemma":[0.9989293,0.0007351715,0.00008746065,0.00004717752,0.00016422,0.00003672721],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004285738,0.00008826548,0.001824697,0.00006010693,0.00006051714,0.00009497449,0.0001492989,0.8872935,0.00935956,0.009577204,0.0003787919,0.09107018],"study_design_scores_gemma":[0.000009021477,0.00002455301,0.0001645246,0.000007861629,0.0000119487,0.00001852903,0.00001648721,0.9939725,0.002179443,0.002951593,0.0006356377,0.000007983521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09977763,0.0001981608,0.8962911,0.0002153386,0.00002747835,0.0001019993,0.00003933555,0.0004959212,0.002853048],"genre_scores_gemma":[0.3281067,0.0003293543,0.6678513,0.0001834307,0.00002144105,0.0002705462,0.0001548651,0.0001386312,0.00294376],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004212184,"threshold_uncertainty_score":0.008375287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01213160546917051,"score_gpt":0.2401985701670045,"score_spread":0.2280669646978339,"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."}}