{"id":"W2232742536","doi":"10.1038/nrn4037","title":"Contrast coding in the electrosensory system: parallels with visual computation","year":2015,"lang":"en","type":"review","venue":"Nature reviews. Neuroscience","topic":"Fish biology, ecology, and behavior","field":"Environmental Science","cited_by":89,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"Canadian Institutes of Health Research","keywords":"Parallels; Contrast (vision); Electric fish; Vertebrate; Coding (social sciences); Neuroscience; Computer science; Sensory system; Neural coding; Communication; Biology; Fish <Actinopterygii>; Artificial intelligence; Psychology; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007176566,0.0009705954,0.001378109,0.001510623,0.0002651246,0.001470041,0.001530347,0.001508014,0.003000974],"category_scores_gemma":[0.001078077,0.0003632954,0.0005002727,0.002008359,0.001450997,0.002376763,0.001217898,0.002011394,0.001583279],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008017133,"about_ca_system_score_gemma":0.001146972,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00109928,"about_ca_topic_score_gemma":0.001503893,"domain_scores_codex":[0.9998274,0.00002662807,0.00001905754,0.00004379826,0.00006296913,0.00002012196],"domain_scores_gemma":[0.9994584,0.0002809896,0.00006991837,0.00002362622,0.0001223356,0.00004474058],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000897634,0.00003276745,0.0003360531,0.009437416,0.0001340208,0.0002006993,0.00006838909,0.001063009,0.003294167,0.02168381,0.02209648,0.9415634],"study_design_scores_gemma":[0.0000239454,0.00007711526,0.002480641,0.003690263,0.0001815438,0.0014425,0.00007551008,0.0004153272,0.001435224,0.03767798,0.9524373,0.00006260029],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000181036,0.9971859,0.0007122478,0.0004996975,0.000149513,0.000002846127,0.00002166733,0.00001490442,0.001232205],"genre_scores_gemma":[0.001771987,0.9969817,0.0003509808,0.0002145442,0.0002319925,0.00000543456,0.00002884431,0.000003852357,0.0004105984],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003000974,"threshold_uncertainty_score":0.01003927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05521024216973505,"score_gpt":0.3582378526598325,"score_spread":0.3030276104900975,"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."}}