{"id":"W2551466753","doi":"10.5539/mas.v11n3p13","title":"Innovative Sketch Board Mining for Online image Retrieval","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universiti Teknologi Malaysia","keywords":"Computer science; Sketch; Information retrieval; Image retrieval; Metadata; Matching (statistics); Precision and recall; Data mining; Image (mathematics); Artificial intelligence; World Wide Web; Algorithm","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009279014,0.0002180032,0.0002359552,0.0002634365,0.0003595944,0.0002141641,0.002018885,0.00006205532,0.000004930616],"category_scores_gemma":[0.0004515676,0.0001521857,0.00004820043,0.002167156,0.0006891015,0.001571586,0.000649216,0.000105666,0.00001750676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001581422,"about_ca_system_score_gemma":0.0002955695,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001217219,"about_ca_topic_score_gemma":0.000001011956,"domain_scores_codex":[0.9973599,0.00001234515,0.0003306243,0.0009738774,0.0006370852,0.0006861887],"domain_scores_gemma":[0.9980236,0.0002372929,0.0001763605,0.0008123926,0.0006049951,0.0001453752],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00002952958,0.00003073893,0.00001331375,0.000004546088,0.000002000681,0.000001826774,0.0002127707,3.466224e-7,0.7709947,0.01321735,0.0002036861,0.2152892],"study_design_scores_gemma":[0.0005735334,0.0001414702,0.0004362085,0.00003444965,0.000002420959,0.000004795898,0.00002604812,0.01420524,0.90421,0.07628655,0.003727894,0.0003514202],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01331818,0.00002467972,0.9829588,0.0009271849,0.0001697177,0.0004701701,0.00001819321,0.0004698399,0.001643254],"genre_scores_gemma":[0.4269234,0.000009951753,0.5721416,0.000457564,0.00006169811,0.00002696129,0.000001051908,0.00001304802,0.0003647404],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4136052,"threshold_uncertainty_score":0.6205955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02650637666793556,"score_gpt":0.3120082053455911,"score_spread":0.2855018286776556,"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."}}