{"id":"W3181489860","doi":"","title":"THE MONTREAL EXPERIENCE : PART I- BASICS AND TREATMENT ALGORITHM","year":2021,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"Computability, Logic, AI Algorithms","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Environmental science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":["sts"],"category_scores_codex":[0.0009493818,0.0003506238,0.0003378436,0.00008386533,0.001619866,0.0005933656,0.001553393,0.00009615361,0.00001987819],"category_scores_gemma":[0.0007183592,0.000246782,0.00007819914,0.001756936,0.007498295,0.0008503199,0.001551819,0.0002212499,0.00002682236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002792695,"about_ca_system_score_gemma":0.001055639,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002304655,"about_ca_topic_score_gemma":0.0000414558,"domain_scores_codex":[0.9960254,0.0004519727,0.000410028,0.001523929,0.0006714212,0.0009172191],"domain_scores_gemma":[0.9969707,0.00090246,0.0001698291,0.0009520139,0.0004692351,0.000535823],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00001813363,0.001111105,0.04532987,0.00001163859,0.0001021343,0.004265093,0.0226107,0.0001387254,0.06085098,0.02295944,0.0001214737,0.8424807],"study_design_scores_gemma":[0.001156455,0.002585237,0.3600878,0.00004776771,0.00002882904,0.004831449,0.002293004,0.2025299,0.2903343,0.1303172,0.004479105,0.00130902],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909732,0.0005470137,0.002958522,0.002196781,0.001057104,0.0003052602,0.00000613795,0.0001210291,0.001834936],"genre_scores_gemma":[0.9461179,0.00006816303,0.05274939,0.0003027552,0.0001452217,0.0001277016,0.000002024174,0.00001133571,0.0004754818],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8411717,"threshold_uncertainty_score":0.9999985,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04589693550888014,"score_gpt":0.3242928598934981,"score_spread":0.278395924384618,"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."}}