{"id":"W2101821946","doi":"10.1369/jhc.2008.950345","title":"Synergistic Tissue Counterstaining and Image Segmentation Techniques for Accurate, Quantitative Immunohistochemistry","year":2008,"lang":"en","type":"article","venue":"Journal of Histochemistry & Cytochemistry","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Segmentation; Immunohistochemistry; Staining; Artificial intelligence; Molecular biology; Pattern recognition (psychology); Computer science; Chemistry; Pathology; Biology; Medicine","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":[0.004885955,0.001731612,0.001185519,0.003120303,0.0007626844,0.001243854,0.001764432,0.001118359,0.003240328],"category_scores_gemma":[0.00553401,0.001852158,0.0008834781,0.001950343,0.001165394,0.001401506,0.001537121,0.00213845,0.001461102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006777113,"about_ca_system_score_gemma":0.001139016,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006828877,"about_ca_topic_score_gemma":0.00259448,"domain_scores_codex":[0.9957509,0.0008409739,0.0003370702,0.0007772039,0.002067652,0.0002260946],"domain_scores_gemma":[0.9962954,0.001392842,0.0005015244,0.0008403347,0.000845188,0.0001247096],"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":[0.0001266196,0.00006360324,0.0005794304,0.0002890235,0.00005675145,0.00007977481,0.0001438703,0.002544574,0.9226812,0.00203149,0.0006689778,0.07073469],"study_design_scores_gemma":[0.00004032096,0.0002659078,0.004130809,0.00003799592,0.00008775477,0.0009141066,0.00004411665,0.07213,0.9082088,0.00186999,0.01216316,0.0001069898],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01408007,0.0002372692,0.9833789,0.00005573205,0.00005632993,0.0001256553,0.00005252435,0.001543024,0.0004703923],"genre_scores_gemma":[0.02139923,0.0001819803,0.9772342,0.00003044348,0.00001774859,0.0002956589,0.00009704359,0.0003167501,0.0004269333],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004885955,"threshold_uncertainty_score":0.02583975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01248751269567988,"score_gpt":0.314962154034509,"score_spread":0.3024746413388291,"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."}}