{"id":"W2333905710","doi":"10.1309/ajcpoxrmk15vcqtr","title":"Recognition and Discrimination of Tissue-Marking Dye Color by Surgical Pathologists","year":2014,"lang":"en","type":"article","venue":"American Journal of Clinical Pathology","topic":"Biological Stains and Phytochemicals","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Orange (colour); Color analysis; Medicine; Surgical margin; Margin (machine learning); Pathology; Dentistry; Surgery; Computer science; Biology; Artificial intelligence","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":[0.01781163,0.0004613301,0.0002772979,0.001102673,0.0004658274,0.0008988399,0.0005095716,0.0008437178,0.001665498],"category_scores_gemma":[0.06067353,0.0002772041,0.0003871972,0.0003552909,0.0007639126,0.001049779,0.001149927,0.0004564214,0.0006414763],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003164628,"about_ca_system_score_gemma":0.0005141211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008632817,"about_ca_topic_score_gemma":0.00137726,"domain_scores_codex":[0.9872953,0.007628345,0.001224769,0.001179531,0.002217338,0.0004546686],"domain_scores_gemma":[0.945132,0.03599823,0.007602131,0.002316932,0.008330031,0.0006207072],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004481718,0.0004309809,0.753612,0.001008311,0.0002675824,0.0004943864,0.02296989,0.0008873061,0.06854069,0.0005921486,0.001455151,0.1452598],"study_design_scores_gemma":[0.0003393936,0.006945646,0.8546759,0.001294026,0.0004517082,0.006811028,0.02958062,0.01292066,0.07327125,0.002563264,0.01083033,0.0003161372],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898633,0.0004568489,0.006895829,0.0001810071,0.00004005848,0.0001516535,0.00004994548,0.00004067088,0.002320797],"genre_scores_gemma":[0.9827896,0.00031448,0.01593048,0.0002077157,0.00002512015,0.0001359209,0.00007318433,0.00001852689,0.0005050966],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01781163,"threshold_uncertainty_score":0.09419799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0640280357306047,"score_gpt":0.3994842511040686,"score_spread":0.3354562153734639,"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."}}