{"id":"W2025668285","doi":"10.1016/j.compmedimag.2007.06.002","title":"Vector edge operators for cDNA microarray spot localization","year":2007,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Gene expression and cancer classification","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Victoria Park","funders":"","keywords":"Complementary DNA; Edge detection; Computer science; Microarray; Support vector machine; Enhanced Data Rates for GSM Evolution; Classification of discontinuities; Pattern recognition (psychology); Artificial intelligence; Biology; Image processing; Mathematics; Gene expression; Gene; Genetics; Image (mathematics)","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":[0.0004110679,0.0001098882,0.0001077459,0.00006569499,0.0001233953,0.00003353668,0.0001216856,0.0001146816,0.000006567427],"category_scores_gemma":[0.00009705173,0.00009828147,0.00005320759,0.0001157242,0.0001363415,0.000003813497,0.00005458515,0.00007548539,6.63002e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005476898,"about_ca_system_score_gemma":0.00006345115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003076891,"about_ca_topic_score_gemma":0.000003314366,"domain_scores_codex":[0.9991519,0.00002783963,0.00019137,0.0002974208,0.0001380368,0.00019336],"domain_scores_gemma":[0.9994642,0.00002272383,0.0000483427,0.0001665156,0.00009468436,0.0002034947],"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":[0.0001859121,0.00009605184,0.004275259,0.00007904856,0.00003512601,0.000004838275,0.0001148399,0.000003822004,0.8552395,0.001550346,0.02671387,0.1117014],"study_design_scores_gemma":[0.002865135,0.000118209,0.00566438,0.0001021356,0.00002612676,0.00004286654,0.000104764,0.01460362,0.1698404,0.0002684631,0.8059756,0.0003882787],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1193333,0.001273823,0.8773829,0.001141471,0.0006187669,0.0001543611,0.000004899289,0.00002679804,0.00006360361],"genre_scores_gemma":[0.9924259,0.0004912624,0.002854561,0.003356935,0.0005827057,0.00001550849,0.0001720183,0.00002034523,0.00008081764],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8745284,"threshold_uncertainty_score":0.4007802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00982513963708688,"score_gpt":0.2771624046728035,"score_spread":0.2673372650357166,"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."}}