{"id":"W2781584338","doi":"10.1073/pnas.1712380115","title":"Motion parallax in electric sensing","year":2018,"lang":"en","type":"article","venue":"Proceedings of the National Academy of Sciences","topic":"Fish biology, ecology, and behavior","field":"Environmental Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Deutsche Forschungsgemeinschaft; Natural Sciences and Engineering Research Council of Canada; Ministerium für Kultur und Wissenschaft des Landes Nordrhein-Westfalen; Government of Canada; Government of Ontario","keywords":"Parallax; Electric fish; Computer science; Peering; Perception; Computer vision; Artificial intelligence; Exploit; Electric field; Fish <Actinopterygii>; Physics; Biology; Neuroscience","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":[0.0001264659,0.0002079967,0.0001640118,0.0001932734,0.0002362831,0.0003870848,0.0003500796,0.0004168295,0.001765643],"category_scores_gemma":[0.0006146014,0.0001393807,0.0001206195,0.0001407998,0.0008131748,0.00102083,0.0005540469,0.000525784,0.0003264993],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003350418,"about_ca_system_score_gemma":0.0001526479,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004216758,"about_ca_topic_score_gemma":0.0004805594,"domain_scores_codex":[0.9998879,0.00001697425,0.000005105697,0.00004004503,0.00003352157,0.00001644403],"domain_scores_gemma":[0.9997818,0.00007228514,0.0000556128,0.00002962607,0.00002661794,0.00003409608],"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":[0.00005963864,0.000009268723,0.0008714699,0.00005807572,0.000002951788,0.0001499806,0.00006158678,0.0006310692,0.9770488,0.009303665,0.0001252461,0.01167828],"study_design_scores_gemma":[0.0001086773,0.001074455,0.1612656,0.0001115424,0.00005624977,0.004465697,0.000728922,0.05780346,0.672718,0.06968346,0.0318044,0.0001794458],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.884032,0.00581924,0.08384845,0.002006174,0.0003022381,0.0000418137,0.0001458358,0.0002642513,0.02354],"genre_scores_gemma":[0.98875,0.000955904,0.007038852,0.0002487581,0.00005298744,0.00001565414,0.00006249386,0.00002655809,0.002848767],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001765643,"threshold_uncertainty_score":0.005906641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03577126706193029,"score_gpt":0.2936152067818916,"score_spread":0.2578439397199613,"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."}}