{"id":"W3193176096","doi":"10.1101/2021.08.05.455311","title":"A general deep learning model for bird detection in high resolution airborne imagery","year":2021,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"U.S. Army Corps of Engineers; South Florida Water Management District; Gordon and Betty Moore Foundation","keywords":"Overfitting; Computer science; Artificial intelligence; Deep learning; Retraining; Machine learning; Scale (ratio); Recall; Ecology; Artificial neural network; Geography; Cartography","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":[0.00100228,0.0008368471,0.0005560133,0.0007842347,0.0003003279,0.0007414843,0.001716846,0.001285635,0.0020497],"category_scores_gemma":[0.001501519,0.0005433052,0.0008334114,0.0007225148,0.0006039664,0.001131011,0.000790457,0.001277754,0.0006898235],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001309395,"about_ca_system_score_gemma":0.0008539265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02178098,"about_ca_topic_score_gemma":0.02852156,"domain_scores_codex":[0.9997566,0.00003898589,0.00001065007,0.0001125809,0.00003833772,0.00004281665],"domain_scores_gemma":[0.9996108,0.000159798,0.00005026459,0.00003407201,0.0001183145,0.00002668669],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007055453,0.00007248306,0.003147878,0.00004068041,0.00005153532,0.00005607011,0.00003227601,0.9483501,0.002889084,0.001909902,0.001946051,0.04143342],"study_design_scores_gemma":[0.000001769584,0.000004082569,0.000196818,0.000002137471,0.000002510923,0.000003777038,0.000001438557,0.9989992,0.0001402242,0.0005544405,0.0000917507,0.000001833699],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2047558,0.000781558,0.7853115,0.001262217,0.0001061171,0.00009587761,0.001509461,0.002067845,0.00410969],"genre_scores_gemma":[0.892728,0.0003099578,0.09156504,0.0005185883,0.00005534019,0.0002145711,0.00199773,0.0001329354,0.01247779],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02178098,"threshold_uncertainty_score":0.04330838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01929289006127724,"score_gpt":0.218304043273934,"score_spread":0.1990111532126568,"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."}}