{"id":"W4394568027","doi":"10.1007/s00530-024-01296-x","title":"RefinerHash: a new hashing-based re-ranking technique for image retrieval","year":2024,"lang":"en","type":"article","venue":"Multimedia Systems","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Ranking (information retrieval); Information retrieval; Hash function; Image retrieval; Cryptography; Image (mathematics); Data mining; Artificial intelligence; Algorithm; Computer security","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001211166,0.0003451979,0.000424335,0.0003773972,0.0001569121,0.000927125,0.001055176,0.0002286627,0.000009754421],"category_scores_gemma":[0.0006424136,0.000302926,0.000235826,0.0009707931,0.0000660414,0.001113987,0.0001529598,0.000358942,0.00006355867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002092053,"about_ca_system_score_gemma":0.0003730645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001035955,"about_ca_topic_score_gemma":0.000003601378,"domain_scores_codex":[0.9973581,0.00009408674,0.0006144901,0.0008649186,0.0004946086,0.0005737419],"domain_scores_gemma":[0.9977276,0.000802237,0.0001438455,0.0009026147,0.0002033174,0.0002203488],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001828085,0.0001343412,0.00004030568,0.002116005,0.0001171122,0.0006067276,0.001747427,0.00009090624,0.7241704,0.005880661,0.08353052,0.1813828],"study_design_scores_gemma":[0.00049713,0.000239285,0.00000494026,0.0009426892,0.00002026637,0.00004875313,0.0000210393,0.4885899,0.3723959,0.001716254,0.1350537,0.0004701344],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00002010048,0.002589713,0.9898872,0.0009310276,0.001561608,0.001840664,0.00002545985,0.002597853,0.0005463618],"genre_scores_gemma":[0.038855,0.00002824126,0.9576659,0.0001887659,0.0009421463,0.0003448799,0.00002779984,0.00008711524,0.00186014],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.488499,"threshold_uncertainty_score":0.9999423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03338403731727232,"score_gpt":0.3167937473687916,"score_spread":0.2834097100515193,"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."}}