{"id":"W7026697463","doi":"","title":"Analysis of the human corneal shape with machine learning","year":2023,"lang":"fr","type":"dissertation","venue":"Papyrus : Institutional Repository (Université de Montréal)","topic":"Ophthalmology and Visual Impairment Studies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Fonds de recherche du Québec – Nature et technologies","keywords":"Zernike polynomials; Statistical analysis","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007363345,0.0003306195,0.0003715815,0.0008035149,0.0002187654,0.001002754,0.0003837131,0.0005200444,0.002321144],"category_scores_gemma":[0.002068445,0.0002289404,0.0007598128,0.0005766763,0.000388028,0.0007417328,0.0004028224,0.0004023903,0.0007727985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006230554,"about_ca_system_score_gemma":0.0006407349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003657224,"about_ca_topic_score_gemma":0.004962017,"domain_scores_codex":[0.9996456,0.0000699432,0.00001511721,0.00006981078,0.0001599571,0.00003959638],"domain_scores_gemma":[0.9990793,0.0004101603,0.00008018392,0.0001558522,0.0002423124,0.00003213657],"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":[0.0002395306,0.00007029947,0.01091033,0.0002139869,0.00008029172,0.0001118778,0.0001819584,0.2428512,0.0466936,0.003021044,0.002072363,0.6935536],"study_design_scores_gemma":[0.000004368317,0.00004147798,0.008732624,0.00001705836,0.000008845948,0.0001164045,0.00004945741,0.9759379,0.01198648,0.001564473,0.001521289,0.00001966959],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2624294,0.001651332,0.7272441,0.0007524966,0.0001499921,0.00008604446,0.0003368783,0.001296659,0.006052962],"genre_scores_gemma":[0.8788465,0.0008318603,0.114747,0.0001197834,0.00006586493,0.00003739049,0.0002693803,0.0001120972,0.004970144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003657224,"threshold_uncertainty_score":0.007765055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01293284944097212,"score_gpt":0.2408944375837708,"score_spread":0.2279615881427987,"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."}}