{"id":"W2357051493","doi":"","title":"Method of Anterior Chamber Diameter Forecasting System Based on B Pneural Network","year":2007,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Advanced Sensor and Control Systems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial neural network; Simulation; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0006350106,0.0005780515,0.0005045044,0.0007675995,0.0004324752,0.0006625835,0.0008462915,0.0007649889,0.002403647],"category_scores_gemma":[0.001404121,0.0002902344,0.0002456363,0.000494238,0.0002311273,0.0007250544,0.0003536064,0.0005035892,0.0007185312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004450073,"about_ca_system_score_gemma":0.0005945876,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007773928,"about_ca_topic_score_gemma":0.00471657,"domain_scores_codex":[0.9996316,0.00007469791,0.00002428428,0.0001157151,0.0001211919,0.00003238788],"domain_scores_gemma":[0.999521,0.0001290848,0.00004857062,0.00003059071,0.0002446061,0.00002605303],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006757827,0.0001333032,0.005324118,0.000236672,0.0001371944,0.000252554,0.0001930466,0.162928,0.03884082,0.003408994,0.007029599,0.7808399],"study_design_scores_gemma":[0.00003596547,0.00005289764,0.001168211,0.00001221928,0.00002278087,0.00008643085,0.00001561434,0.9908857,0.005571885,0.0008484975,0.001275137,0.00002466965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01752354,0.0002707075,0.9769173,0.0001659141,0.00009480026,0.0001031272,0.0001084788,0.002244244,0.002571883],"genre_scores_gemma":[0.5300812,0.000491412,0.4612837,0.0001687168,0.0001350287,0.0004645745,0.0003267791,0.00009993423,0.006948659],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007773928,"threshold_uncertainty_score":0.01545739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01012460001399005,"score_gpt":0.2337752044967051,"score_spread":0.223650604482715,"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."}}