{"id":"W2367601863","doi":"","title":"A Method of Image Retrieval Based on Partitioned Binarization","year":2008,"lang":"en","type":"article","venue":"Microcomputer applications","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Image retrieval; Feature (linguistics); Image (mathematics); Precision and recall; Pattern recognition (psychology); Binary number; Computer vision; Process (computing); Binary image; Image processing; Mathematics; Arithmetic","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":[0.0002080783,0.0001328156,0.0001655183,0.0002068525,0.0002118572,0.00004180899,0.000634859,0.00007009534,0.00001290012],"category_scores_gemma":[0.000002495048,0.0001300439,0.000096366,0.001037705,0.00008588615,0.0002220517,0.00008333792,0.0001030138,0.00007376615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000432463,"about_ca_system_score_gemma":0.0000981586,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003860553,"about_ca_topic_score_gemma":8.912674e-8,"domain_scores_codex":[0.9987839,0.00008056092,0.0003394673,0.0003924191,0.0002403852,0.0001632393],"domain_scores_gemma":[0.9987634,0.00009481914,0.0001835401,0.0006005787,0.0002919437,0.00006572555],"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":[0.00004277949,0.001252203,0.0001920392,0.00008865535,0.00002711931,0.000005966398,0.0005923599,0.0001991695,0.7824932,0.09015765,0.002156139,0.1227927],"study_design_scores_gemma":[0.000383011,0.0001216618,0.001814667,0.00002207031,0.000007925601,0.00002810327,0.000003094256,0.09020223,0.8397108,0.003157915,0.06432345,0.000225053],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001391668,0.00001673187,0.9971913,0.001108495,0.000009658189,0.0005190722,0.000009215198,0.0003615838,0.0006447898],"genre_scores_gemma":[0.05647165,0.00001218103,0.9427162,0.0004917489,0.00004402945,0.0001317856,0.00002714472,0.0000111052,0.00009417215],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1225676,"threshold_uncertainty_score":0.5303037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01687128170244452,"score_gpt":0.2741984491382761,"score_spread":0.2573271674358316,"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."}}