{"id":"W4210926750","doi":"10.1101/2022.02.07.479461","title":"CameraTrapDetectoR: Automatically detect, classify, and count animals in camera trap images using artificial intelligence","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Wildlife Ecology and Conservation","field":"Environmental Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; National Capital Commission","funders":"","keywords":"Camera trap; Artificial intelligence; Computer science; Bounding overwatch; Computer vision; Minimum bounding box; Deep learning; Object (grammar); Wildlife; Pattern recognition (psychology); Class (philosophy); Image (mathematics); Ecology; Biology","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":[0.001140453,0.002413274,0.0007436289,0.00126231,0.0002623769,0.001146134,0.002376764,0.0008252469,0.01549023],"category_scores_gemma":[0.003706804,0.0009251084,0.00143516,0.0007895281,0.0003698742,0.001163482,0.001253187,0.001638932,0.01037902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001025398,"about_ca_system_score_gemma":0.001287451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01155047,"about_ca_topic_score_gemma":0.01379971,"domain_scores_codex":[0.9994118,0.00007406173,0.00003217191,0.0002734782,0.0001533502,0.00005510954],"domain_scores_gemma":[0.9990129,0.0004611151,0.0001232616,0.0001388571,0.0001927262,0.00007109698],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007944306,0.0002894395,0.02033942,0.001036827,0.0009150798,0.000223946,0.0001576924,0.1131044,0.01543672,0.002595452,0.4961117,0.348995],"study_design_scores_gemma":[0.0001256263,0.0001683648,0.005186833,0.0001011983,0.0001064928,0.0001499698,0.00002401763,0.9332705,0.02412628,0.002992033,0.03366162,0.00008715726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"software","genre_gemma":"methods","genre_scores_codex":[0.03641546,0.001189631,0.4107904,0.0006584185,0.0004114317,0.0004405376,0.05279917,0.4911411,0.006153798],"genre_scores_gemma":[0.2370772,0.0009767846,0.5943463,0.001420485,0.0001676436,0.00196103,0.1230844,0.02408172,0.01688443],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01549023,"threshold_uncertainty_score":0.05181998,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02270265562780522,"score_gpt":0.2401486393303277,"score_spread":0.2174459837025225,"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."}}