{"id":"W4401383180","doi":"10.1038/s41467-024-51178-z","title":"Interspecies-chimera machine vision with polarimetry for real-time navigation and anti-glare pattern recognition","year":2024,"lang":"en","type":"article","venue":"Nature Communications","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Henan Normal University","keywords":"Chimera (genetics); Polarimetry; Computer science; Computer vision; Artificial intelligence; Machine vision; Physics; Optics; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009511586,0.0001122553,0.0001010257,0.00008472659,0.0001775869,0.00006570526,0.000170409,0.0001101855,0.000006532119],"category_scores_gemma":[0.00001870111,0.00009925322,0.00003095615,0.0002016794,0.00003639069,0.0002399833,0.00006915555,0.0005882558,0.000008806772],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004016964,"about_ca_system_score_gemma":0.000006535141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003565504,"about_ca_topic_score_gemma":0.00000976114,"domain_scores_codex":[0.9995385,0.00002879921,0.0001246243,0.0001434002,0.00006000457,0.0001046605],"domain_scores_gemma":[0.9992302,0.0002791208,0.00002103709,0.0003816414,0.00005461017,0.00003338354],"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":[0.00005409877,0.00009799381,0.0006061218,0.0007769455,0.0002479972,0.00001144372,0.001452716,0.001303046,0.2462928,0.001138388,0.003643729,0.7443748],"study_design_scores_gemma":[0.001287751,0.0003950601,0.006682059,0.003296015,0.0002812406,0.0002586941,0.000317862,0.8737804,0.04263451,0.001805597,0.06805271,0.001208075],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9167765,0.02615979,0.04895717,0.002246665,0.0005065789,0.0008937555,0.0003703729,0.00168289,0.00240623],"genre_scores_gemma":[0.9843302,0.0006458152,0.0141847,0.00004942989,0.00005824507,0.000005861913,0.0006634322,0.00003327351,0.00002901888],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8724774,"threshold_uncertainty_score":0.4047429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0141036728527906,"score_gpt":0.291682858837773,"score_spread":0.2775791859849824,"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."}}