{"id":"W4205675430","doi":"10.1139/juvs-2021-0024","title":"Detection and tracking of belugas, kayaks and motorized boats in drone video using deep learning","year":2022,"lang":"en","type":"article","venue":"Drone Systems and Applications","topic":"Marine animal studies overview","field":"Environmental Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Churchill Northern Studies Centre; Canada Excellence Research Chairs, Government of Canada","keywords":"Drone; Artificial intelligence; Computer science; Deep learning; Computer vision; Convolutional neural network; Video tracking; Tracking (education); Object detection; Object (grammar); Pattern recognition (psychology)","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0003730178,0.0004134613,0.0001778999,0.0009948792,0.0002427348,0.000418505,0.0003643017,0.000286649,0.0006388358],"category_scores_gemma":[0.0009079186,0.0001663472,0.0002213423,0.000393766,0.0002160021,0.0003910139,0.0003714369,0.0002764255,0.0003369247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004077183,"about_ca_system_score_gemma":0.0003822489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03099774,"about_ca_topic_score_gemma":0.08925835,"domain_scores_codex":[0.9997942,0.00002926564,0.000008296011,0.00009001969,0.00004909943,0.00002905172],"domain_scores_gemma":[0.9996608,0.0000996837,0.00005315358,0.000050313,0.0001081685,0.00002783228],"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":[0.000357489,0.0002128509,0.21371,0.000353811,0.0002623792,0.0004267153,0.0007655945,0.05778997,0.09751952,0.001171283,0.005936682,0.6214936],"study_design_scores_gemma":[0.00002000088,0.0002760775,0.2560445,0.0001381578,0.0001093051,0.0005832728,0.0007132725,0.6778924,0.05471756,0.001135322,0.008313191,0.00005688893],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8767003,0.0006945744,0.1133423,0.0001801813,0.0000775813,0.00009651339,0.001517728,0.001523039,0.005867762],"genre_scores_gemma":[0.9258451,0.0003212573,0.06807694,0.00008847146,0.00001055176,0.00003619815,0.002568712,0.00003249265,0.003020172],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03099774,"threshold_uncertainty_score":0.06163466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01506454879873872,"score_gpt":0.2324818606906754,"score_spread":0.2174173118919366,"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."}}