{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00152481,0.0005657969,0.0007799109,0.0008966369,0.0006168701,0.0005813401,0.002068971,0.001146807,0.002021667],"category_scores_gemma":[0.003574578,0.0003723806,0.0007301958,0.001236479,0.0007167927,0.001033806,0.001024745,0.001035574,0.001283059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008032466,"about_ca_system_score_gemma":0.001124583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002934458,"about_ca_topic_score_gemma":0.004451838,"domain_scores_codex":[0.9987773,0.0003146452,0.0000714727,0.0003287961,0.0004313777,0.00007640594],"domain_scores_gemma":[0.9988866,0.0003983732,0.0001069613,0.0002052213,0.0003638697,0.00003892828],"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":[0.0001955497,0.0001049089,0.0006084819,0.0001032946,0.00006776796,0.00006486408,0.0001351302,0.1584424,0.02658744,0.01482906,0.003364488,0.7954966],"study_design_scores_gemma":[0.0000104826,0.0000385854,0.0002644373,0.000004502685,0.000009214764,0.00005328986,0.000007748133,0.9878862,0.005509192,0.004124941,0.002075177,0.00001621761],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002349409,0.00006234952,0.996857,0.00004733229,0.00001841306,0.00003445354,0.00003085004,0.0003544605,0.0002456433],"genre_scores_gemma":[0.07254808,0.000164819,0.9232019,0.0001052736,0.00006360443,0.0003498017,0.0002746798,0.00006702141,0.003224699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002934458,"threshold_uncertainty_score":0.008064032,"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."}}