{"id":"W3049037379","doi":"10.1109/cjece.2019.2925780","title":"A Study of Dimensionality Reduction Impact on an Approach to People Detection in Gigapixel Images","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Electrical and Computer Engineering","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico; Fundação de Amparo à Pesquisa do Estado de Goiás; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Dimensionality reduction; Reduction (mathematics); Computer science; Artificial intelligence; Business; Computer vision; Risk analysis (engineering); Mathematics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0022697,0.0006616347,0.0005812725,0.001429408,0.0005237143,0.000926143,0.0004622454,0.0005690505,0.0009434259],"category_scores_gemma":[0.01051315,0.0001814537,0.0007141329,0.0009686499,0.0004438311,0.001142388,0.0006037215,0.0007024793,0.0003442094],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006691703,"about_ca_system_score_gemma":0.0003135898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004198885,"about_ca_topic_score_gemma":0.003131872,"domain_scores_codex":[0.9980608,0.0005686693,0.000124365,0.0003383101,0.0007552435,0.0001526809],"domain_scores_gemma":[0.9932769,0.004329985,0.000372491,0.0007264636,0.001158733,0.0001354221],"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":[0.002146199,0.001214743,0.04737538,0.0007242653,0.0006204938,0.000595123,0.0009381431,0.1473008,0.06512144,0.002250525,0.007903134,0.7238097],"study_design_scores_gemma":[0.00005428669,0.001573489,0.08388726,0.0000842938,0.0002401009,0.000839556,0.0007855469,0.8462858,0.05946404,0.002211022,0.004508705,0.00006594696],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9323906,0.00305781,0.05818723,0.0006179468,0.0001358651,0.0001771254,0.0004466984,0.0007770643,0.004209665],"genre_scores_gemma":[0.9364294,0.0008028869,0.06017536,0.0001110602,0.00006950097,0.00006099316,0.0008047892,0.0000732156,0.001472701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004198885,"threshold_uncertainty_score":0.01200348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01418463382714174,"score_gpt":0.229051177790447,"score_spread":0.2148665439633052,"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."}}