{"id":"W4386095521","doi":"10.1111/2041-210x.14187","title":"RFIDeep: Unfolding the potential of deep learning for radio‐frequency identification","year":2023,"lang":"en","type":"article","venue":"Methods in Ecology and Evolution","topic":"Avian ecology and behavior","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"Centre National de la Recherche Scientifique; Centre Scientifique de Monaco; Academy of Finland; Institut Polaire Français Paul Emile Victor; Deutsche Forschungsgemeinschaft","keywords":"Identification (biology); Deep learning; Phenology; Field (mathematics); Computer science; Workflow; Machine learning; Artificial intelligence; Convolutional neural network; Seabird; Citizen science; Data science; Ecology; Biology; Database","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002329121,0.00005800211,0.0001084549,0.00006525319,0.0002575547,0.000003884552,0.00009886976,0.0001367447,0.0001137536],"category_scores_gemma":[0.0004116871,0.00004895111,0.00003213424,0.0002681376,0.0002654902,0.0001055368,0.00006241084,0.0001340287,0.00002703046],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009023774,"about_ca_system_score_gemma":0.000005334376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002500107,"about_ca_topic_score_gemma":0.0002483861,"domain_scores_codex":[0.998929,0.0004362406,0.0002187661,0.0001818766,0.00004371402,0.0001903518],"domain_scores_gemma":[0.9994199,0.0003602121,0.0001078647,0.00008779118,0.000007389406,0.00001679942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001447407,0.00001908976,0.9160211,0.000006166227,0.000004203971,0.000001029118,0.0002263753,0.003447389,0.05300646,0.0003046639,0.00002597192,0.02692306],"study_design_scores_gemma":[0.000186914,0.00005601233,0.9681769,0.000001695025,0.00002519524,0.000007184927,0.0004011987,0.01474607,0.0009006165,0.01540881,0.00003889918,0.00005053046],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8658912,0.00005104345,0.1332149,0.0002086059,0.0003326468,0.0002114623,6.709033e-7,0.00002131706,0.00006813354],"genre_scores_gemma":[0.980116,0.00002833754,0.019523,0.00001526003,0.00001906363,0.00008170056,0.000006197985,0.000004874554,0.0002055309],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1142248,"threshold_uncertainty_score":0.1996168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02003220274981728,"score_gpt":0.3315019204796404,"score_spread":0.3114697177298231,"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."}}