{"id":"W2087916749","doi":"10.1016/j.patrec.2008.12.015","title":"Ore image segmentation by learning image and shape features","year":2009,"lang":"en","type":"article","venue":"Pattern Recognition Letters","topic":"Mineral Processing and Grinding","field":"Engineering","cited_by":42,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Artificial intelligence; Segmentation; Computer vision; Image segmentation; Scale-space segmentation; Segmentation-based object categorization; Image (mathematics); Computer science; Pattern recognition (psychology); Region growing; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006248793,0.0001307443,0.00009113464,0.00007031032,0.00009200972,0.0001425921,0.00003741562,0.00004047262,0.0001397356],"category_scores_gemma":[0.000009466266,0.0001364549,0.00002377618,0.00005708546,0.00001846593,0.0003028183,0.000005809606,0.0002078556,0.00007294726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002392658,"about_ca_system_score_gemma":0.000001087267,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000612108,"about_ca_topic_score_gemma":9.260115e-7,"domain_scores_codex":[0.9994301,0.00002141093,0.0001195407,0.0001548682,0.00009449443,0.000179611],"domain_scores_gemma":[0.9998425,0.00001977487,0.00003076353,0.00004150298,0.00001561001,0.0000498775],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002441401,0.000005087309,0.0003591124,0.00004857371,0.000007946457,0.000007168434,0.0002797207,0.00007393547,0.6245918,4.948775e-8,0.01289953,0.3617246],"study_design_scores_gemma":[0.006908495,0.0005234869,0.114266,0.001828737,0.0003306615,0.00044708,0.002207179,0.1043492,0.7594432,0.0006435681,0.004339852,0.004712611],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9850373,0.0001516733,0.01238519,0.001318562,0.00007854062,0.00007422001,0.00001586758,0.0002802484,0.0006584366],"genre_scores_gemma":[0.9959607,0.0000658754,0.001406597,0.00210675,0.0001263531,0.00000831648,0.0002551513,0.00002278586,0.00004747431],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.357012,"threshold_uncertainty_score":0.556447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007710042616262906,"score_gpt":0.2172710687612338,"score_spread":0.2095610261449709,"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."}}