{"id":"W1994479093","doi":"10.1142/s0219878904000033","title":"VISUAL INFORMATION ACQUISITION IN VERTEBRATE RETINA","year":2004,"lang":"en","type":"article","venue":"International Journal of Information Acquisition","topic":"CCD and CMOS Imaging Sensors","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Retina; Neurophysiology; Vertebrate; Artificial neural network; Neuroscience; Retinal; Artificial intelligence; Computer vision; Biology","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":[0.00007015037,0.0001883675,0.000210319,0.0001268386,0.0001832913,0.0003292311,0.0004027203,0.0004031703,0.0007050904],"category_scores_gemma":[0.0002261912,0.0001174197,0.0003589263,0.000100184,0.0003044368,0.0007185613,0.0002327068,0.0002746248,0.0001266992],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005945261,"about_ca_system_score_gemma":0.0003299557,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003015352,"about_ca_topic_score_gemma":0.001613792,"domain_scores_codex":[0.9999387,0.000007891346,0.000002163392,0.00002139343,0.00002267474,0.000007166764],"domain_scores_gemma":[0.999954,0.00001174235,0.00001067979,0.000005757663,0.00001341981,0.000004326704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.000111187,0.0000389108,0.002430874,0.0002317782,0.00007828598,0.0005239535,0.0001876687,0.6263029,0.1992391,0.1331676,0.0008021104,0.03688573],"study_design_scores_gemma":[0.000010354,0.00006072749,0.002078907,0.00001146847,0.00002066943,0.0002414876,0.00002491429,0.9676864,0.01016314,0.01769594,0.001993227,0.00001281019],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2567616,0.002085433,0.7230033,0.0004821518,0.00006587961,0.00002809512,0.0001382445,0.0002581303,0.01717716],"genre_scores_gemma":[0.9530635,0.001150222,0.04101551,0.00006794045,0.00001806992,0.00002748836,0.00006612842,0.00001998015,0.004571089],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003015352,"threshold_uncertainty_score":0.005995572,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002652047561952133,"score_gpt":0.2198734544491426,"score_spread":0.2172214068871905,"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."}}