{"id":"W36357238","doi":"","title":"An Unsupervised Learning Scheme for DNA Microarray Image Spot Detection","year":2005,"lang":"en","type":"article","venue":"","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Artificial intelligence; DNA microarray; Cluster analysis; Computer science; Image segmentation; Histogram; Pattern recognition (psychology); Segmentation; Pixel; Region growing; Segmentation-based object categorization; Noise (video); Microarray databases; Scale-space segmentation; Computer vision; Image (mathematics); Biology; Gene expression; Genetics; Gene","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000174492,0.00009772884,0.00009906849,0.0000590072,0.0005272027,0.0002797987,0.00007950486,0.00003146663,0.00459434],"category_scores_gemma":[0.00001822318,0.00008592851,0.00006664848,0.00001851215,0.0000670755,0.0004492124,0.00001042394,0.00009018302,0.000170731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003193043,"about_ca_system_score_gemma":0.00001392076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001318235,"about_ca_topic_score_gemma":0.01066908,"domain_scores_codex":[0.9993902,0.0000295481,0.0001458854,0.0001731415,0.00007449077,0.0001867388],"domain_scores_gemma":[0.9996486,0.00002015597,0.00003517903,0.0001014692,0.0001377978,0.00005684025],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004959372,0.0001809921,0.0003022306,0.00003067517,0.00003694072,7.701908e-7,0.006758722,0.00001310925,0.9403547,0.02588128,0.00206055,0.02433043],"study_design_scores_gemma":[0.0005993095,0.000228554,0.0004867685,0.0000058056,0.00001440737,8.331412e-7,0.002141328,0.005996021,0.1737793,0.0004502377,0.8160654,0.0002319781],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9621731,0.00001697664,0.0009232215,0.0002700913,0.0001735172,0.0001862687,0.000007794807,0.000126639,0.0361224],"genre_scores_gemma":[0.975067,0.00000458554,0.0008769433,0.0001963835,0.0008781356,0.00002015915,0.00001455839,0.00001832736,0.02292393],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8140049,"threshold_uncertainty_score":0.9963156,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02347473808355319,"score_gpt":0.2520065689646514,"score_spread":0.2285318308810982,"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."}}