{"id":"W2138707709","doi":"10.1186/1471-2202-15-s1-p185","title":"Neural coding strategies for extracting motion estimates from electrosensory contrast","year":2014,"lang":"en","type":"article","venue":"BMC Neuroscience","topic":"Fish biology, ecology, and behavior","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Stimulus (psychology); Neural coding; Looming; Electric fish; Population; Artificial intelligence; Coding (social sciences); Computer science; Neuroscience; Decoding methods; Communication; Pattern recognition (psychology); Computer vision; Mathematics; Psychology; Biology; Algorithm; Statistics","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":[0.0003454025,0.0003288949,0.0001892222,0.0005790984,0.0001597397,0.000755303,0.0005518529,0.0003876839,0.0006167],"category_scores_gemma":[0.001757037,0.00020186,0.0002239856,0.0003249956,0.0006841459,0.0009340121,0.0006832376,0.0005433259,0.0001413442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000451125,"about_ca_system_score_gemma":0.000204049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004664139,"about_ca_topic_score_gemma":0.0006842841,"domain_scores_codex":[0.9998641,0.00002394986,0.00001127118,0.00004693728,0.00004024922,0.00001334215],"domain_scores_gemma":[0.9995351,0.000158851,0.0001271322,0.00006912149,0.00007866731,0.00003114465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001134257,0.00004435599,0.004605865,0.0001484177,0.00005999013,0.0001184664,0.0002705253,0.01779084,0.7678046,0.02645845,0.0003411717,0.182244],"study_design_scores_gemma":[0.00004398666,0.0002809488,0.04948084,0.00009360572,0.0001243799,0.0006366039,0.0001670411,0.6020544,0.240612,0.10322,0.003154897,0.0001312408],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2865025,0.000743575,0.7064041,0.0003855048,0.0000496277,0.00006347734,0.0001240473,0.0003343735,0.005392832],"genre_scores_gemma":[0.8669526,0.0002983924,0.1317277,0.0001093876,0.00004609068,0.00004460374,0.00008015476,0.00007095138,0.0006701158],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.000755303,"threshold_uncertainty_score":0.003273129,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04331581570035641,"score_gpt":0.2829434739773938,"score_spread":0.2396276582770374,"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."}}