{"id":"W3086844398","doi":"10.3390/s20185280","title":"Remote Insects Trap Monitoring System Using Deep Learning Framework and IoT","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Date Palm Research Studies","field":"Agricultural and Biological Sciences","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Singapore University of Technology and Design; Agency for Science, Technology and Research","keywords":"Convolutional neural network; Computer science; Artificial intelligence; Deep learning; Object detection; Field (mathematics); Trap (plumbing); Internet of Things; Real-time computing; Embedded system; Pattern recognition (psychology); Engineering","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":[0.0001794467,0.0004779798,0.0003708058,0.000407249,0.0002224866,0.0003476952,0.0006611525,0.0003694818,0.001199173],"category_scores_gemma":[0.0002087298,0.0002010883,0.0003592438,0.0002374971,0.0001366423,0.0006096142,0.0005184317,0.0003872211,0.0002634131],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004389742,"about_ca_system_score_gemma":0.0005090543,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006018248,"about_ca_topic_score_gemma":0.007473107,"domain_scores_codex":[0.9998178,0.00001007559,0.00001047671,0.00005553435,0.000068341,0.00003784565],"domain_scores_gemma":[0.9999182,0.000009834863,0.00001455821,0.00001014697,0.00003637475,0.00001086643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006399257,0.0007664085,0.0206732,0.0003348631,0.0001894089,0.0007738986,0.0001848286,0.1258629,0.1602888,0.002481196,0.01166448,0.6761401],"study_design_scores_gemma":[0.00002701422,0.0002116317,0.008124449,0.00001783496,0.00005337514,0.0001981154,0.00003677011,0.9643668,0.02345753,0.0007760134,0.00270079,0.00002975841],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3055314,0.001042767,0.6698445,0.0003752621,0.0001540165,0.0002212438,0.0006951015,0.01027771,0.01185797],"genre_scores_gemma":[0.9121382,0.0003407376,0.08029594,0.000250319,0.00003605035,0.0001258584,0.0008024453,0.00004441647,0.005965995],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006018248,"threshold_uncertainty_score":0.01196647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06630199852606261,"score_gpt":0.2757370786748023,"score_spread":0.2094350801487397,"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."}}