{"id":"W2152734902","doi":"10.1109/ccece.1999.808164","title":"Mobile agents for Web-based medical image retrieval","year":2003,"lang":"en","type":"article","venue":"","topic":"Mobile Agent-Based Network Management","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Image retrieval; Software; Server; Set (abstract data type); IBM; Simple (philosophy); Image (mathematics); Mobile device; Telemedicine; Client; Mobile agent; Software agent; Information retrieval; Computer vision; Artificial intelligence; World Wide Web; Distributed computing; Operating system","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007620993,0.0004719261,0.0006402755,0.0008650738,0.0005842377,0.002119774,0.001359369,0.001580007,0.01185373],"category_scores_gemma":[0.002345968,0.0003158579,0.0004007797,0.0008425584,0.0006523507,0.002023146,0.0009159205,0.001089794,0.006431496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005671078,"about_ca_system_score_gemma":0.0005898898,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001353052,"about_ca_topic_score_gemma":0.001739328,"domain_scores_codex":[0.9993429,0.0002315922,0.00004381605,0.00005999839,0.0002899946,0.00003172724],"domain_scores_gemma":[0.9991794,0.0003712812,0.00008990028,0.0001197196,0.0001813768,0.00005821961],"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":[0.0002866863,0.0002169162,0.0006374516,0.001265725,0.0001473669,0.001127554,0.0004245941,0.02028004,0.02195136,0.255167,0.07226199,0.6262334],"study_design_scores_gemma":[0.0001695254,0.0002142462,0.0006712573,0.0003355531,0.00009761111,0.001531047,0.0001876679,0.1637949,0.01000421,0.1079193,0.7149819,0.00009272356],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004337268,0.01561025,0.9295068,0.002815571,0.0009818001,0.0005973097,0.0002712082,0.004558531,0.04132105],"genre_scores_gemma":[0.1103403,0.01052743,0.8299504,0.001168223,0.0008735777,0.0008093289,0.0007085268,0.0004171288,0.04520527],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01185373,"threshold_uncertainty_score":0.03965467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01584769874985598,"score_gpt":0.2705186396498331,"score_spread":0.2546709408999772,"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."}}