{"id":"W4205894626","doi":"10.1109/vcip53242.2021.9675344","title":"DFTS2: Deep Feature Transmission Simulation for Collaborative Intelligence","year":2021,"lang":"en","type":"article","venue":"2021 International Conference on Visual Communications and Image Processing (VCIP)","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Computer science; Channel (broadcasting); Packet loss; Enhanced Data Rates for GSM Evolution; Feature (linguistics); Inference; Network packet; Transmission (telecommunications); Artificial intelligence; Imperfect; Path (computing); Cloud computing; Real-time computing; Machine learning; Data mining; Distributed computing; Computer network; Telecommunications","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":[0.0008960053,0.0005866052,0.000557453,0.0003933152,0.0005659782,0.0007106575,0.001434515,0.00108493,0.003695461],"category_scores_gemma":[0.003355647,0.0003146071,0.0007325389,0.0003447692,0.0006394665,0.0008948637,0.0008249741,0.00120096,0.0002689589],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001055199,"about_ca_system_score_gemma":0.001330333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01348884,"about_ca_topic_score_gemma":0.007459019,"domain_scores_codex":[0.9997411,0.0001009474,0.00001422892,0.00003136316,0.00007474828,0.00003767012],"domain_scores_gemma":[0.9984291,0.001104231,0.0000870988,0.0001263156,0.0001708817,0.00008226758],"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":[0.00005145085,0.00003246029,0.0007598042,0.00001701932,0.00001258939,0.00002624175,0.00003625156,0.9875545,0.0007097918,0.006479922,0.0005953332,0.00372468],"study_design_scores_gemma":[0.000003165847,0.000003683275,0.00001658233,6.731632e-7,6.309319e-7,0.000001740905,0.000001890787,0.9991583,0.0001600042,0.0005411374,0.0001110347,0.000001101445],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1431009,0.0001339203,0.8439982,0.0004574068,0.0001169056,0.000136667,0.0005971774,0.003100837,0.008358068],"genre_scores_gemma":[0.8468791,0.0001010042,0.1496527,0.0001157534,0.00002384437,0.0002203964,0.0005214696,0.000303281,0.002182523],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01348884,"threshold_uncertainty_score":0.02682066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03738514842136979,"score_gpt":0.3969215276612447,"score_spread":0.3595363792398749,"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."}}