{"id":"W3041519490","doi":"10.1007/978-3-030-51935-3_31","title":"Object Detector Combination for Increasing Accuracy and Detecting More Overlapping Objects","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science; Pascal (unit); Bounding overwatch; Detector; Artificial intelligence; Convolutional neural network; Object detection; Computer vision; Classifier (UML); Pattern recognition (psychology); Object (grammar); Telecommunications; Programming language","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":[0.001827345,0.002672536,0.002808045,0.004494201,0.0008945078,0.002747065,0.002321847,0.002753585,0.009887088],"category_scores_gemma":[0.002033738,0.001478417,0.002126294,0.003499948,0.0004975245,0.002508547,0.00214466,0.001551633,0.006370521],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009073919,"about_ca_system_score_gemma":0.001205568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002224436,"about_ca_topic_score_gemma":0.004772619,"domain_scores_codex":[0.99818,0.0001589676,0.0001132627,0.0005774221,0.0007343212,0.0002360366],"domain_scores_gemma":[0.9984789,0.0004245876,0.00007083894,0.0002352247,0.0006829008,0.000107533],"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":[0.0006074726,0.00042447,0.002628017,0.0004250418,0.0004877792,0.0003207152,0.00005392511,0.007563259,0.1962675,0.001521616,0.007825349,0.781875],"study_design_scores_gemma":[0.00009540185,0.0006240046,0.008832313,0.000101111,0.001594921,0.002529439,0.0001084575,0.4948936,0.4607017,0.003556018,0.02679979,0.0001633247],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05879712,0.004177674,0.9170471,0.000432749,0.001127589,0.0003391309,0.000748613,0.008677149,0.008652797],"genre_scores_gemma":[0.2223387,0.001571938,0.7539952,0.0008421476,0.0003650595,0.0002059175,0.001955071,0.000868988,0.01785702],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009887088,"threshold_uncertainty_score":0.03307563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02219100160590717,"score_gpt":0.2721452976698783,"score_spread":0.2499542960639711,"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."}}