{"id":"W3097582741","doi":"10.1002/ece3.6840","title":"Automated facial recognition for wildlife that lack unique markings: A deep learning approach for brown bears","year":2020,"lang":"en","type":"article","venue":"Ecology and Evolution","topic":"Face recognition and analysis","field":"Computer Science","cited_by":106,"is_retracted":false,"has_abstract":true,"ca_institutions":"Raincoast Conservation Foundation; University of Victoria","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Computer science; Ursus; Deep learning; Classifier (UML); Landmark; Pattern recognition (psychology); Identification (biology); Facial recognition system; Support vector machine; Embedding; Computer vision; Machine learning; Ecology; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0007830085,0.0006682386,0.0003971243,0.0007898321,0.0003559972,0.0004009563,0.0007062188,0.0003635923,0.001010129],"category_scores_gemma":[0.000706348,0.0002204358,0.0003922438,0.0003250758,0.0002880155,0.0004524769,0.0007305862,0.0005590205,0.0003134843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004846562,"about_ca_system_score_gemma":0.0005690107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01451629,"about_ca_topic_score_gemma":0.01918106,"domain_scores_codex":[0.9997773,0.00003804103,0.000008864003,0.00008315314,0.00004703514,0.00004567023],"domain_scores_gemma":[0.9997271,0.00007968321,0.00002658764,0.00003565588,0.0001053726,0.00002568397],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005469686,0.0006001788,0.0604426,0.00009511154,0.000264353,0.0005677426,0.0003651172,0.2082532,0.06850736,0.001366131,0.007904004,0.6510872],"study_design_scores_gemma":[0.00001063427,0.00007932429,0.01238411,0.0000111571,0.00003497498,0.00006654336,0.0001250576,0.9737211,0.01181103,0.000820972,0.0009239499,0.00001124919],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8684135,0.0005428547,0.1262355,0.0005128261,0.000085352,0.00007855111,0.0003464724,0.001808252,0.001976748],"genre_scores_gemma":[0.9296671,0.0001551164,0.06634828,0.0001352487,0.00003074752,0.00004003507,0.0006487902,0.00005680642,0.002917929],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01451629,"threshold_uncertainty_score":0.02886361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03680559932852202,"score_gpt":0.2509601660872971,"score_spread":0.2141545667587751,"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."}}