{"id":"W2584725353","doi":"","title":"Light, What Is It Good For?","year":2016,"lang":"en","type":"article","venue":"Investigative Ophthalmology & Visual Science","topic":"History of Science and Medicine","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science","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":[0.009430782,0.0007284314,0.001075933,0.001890902,0.008548916,0.01217137,0.001455366,0.007241441,0.01083167],"category_scores_gemma":[0.01936072,0.00040619,0.0009496689,0.001229251,0.02859556,0.01590584,0.003640394,0.01826677,0.004356974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007311387,"about_ca_system_score_gemma":0.008730408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01258096,"about_ca_topic_score_gemma":0.02032201,"domain_scores_codex":[0.9950581,0.002001416,0.0002396895,0.000626918,0.00129589,0.0007779653],"domain_scores_gemma":[0.9916293,0.002782098,0.0006073816,0.0007174457,0.002413005,0.001850658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002008402,0.0002328694,0.00492476,0.001275457,0.0001573929,0.0004335682,0.01104904,0.0001384131,0.001299571,0.2920175,0.5507807,0.1374899],"study_design_scores_gemma":[0.00004016821,0.00007153311,0.00232125,0.001552199,0.00005936595,0.0005081692,0.01681917,0.00006122929,0.0005647456,0.1575218,0.8204035,0.00007684943],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.004302852,0.07453877,0.002170454,0.8291945,0.01706098,0.00002906291,0.0001811659,0.0001398299,0.07238229],"genre_scores_gemma":[0.2231877,0.09799977,0.007170509,0.5653912,0.02610089,0.0001548545,0.0002354669,0.0006980454,0.07906143],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01258096,"threshold_uncertainty_score":0.05304807,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09408546101411974,"score_gpt":0.343625918871477,"score_spread":0.2495404578573572,"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."}}