{"id":"W2221852422","doi":"","title":"Minimal Loss Hashing for Compact Binary Codes","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":704,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Hash function; Binary code; Computer science; Binary number; Code (set theory); Algorithm; Hash table; Theoretical computer science; Similarity (geometry); Hinge loss; Artificial intelligence; Image (mathematics); Mathematics; Arithmetic; Set (abstract data type)","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001755603,0.0001027559,0.0001307504,0.00006144702,0.00009635244,0.00006113922,0.0006045552,0.00003702491,0.00002922509],"category_scores_gemma":[0.00004623278,0.00008283329,0.00006945008,0.0001524392,0.00004768212,0.0009152917,0.0001115286,0.00006085195,0.00001859564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001639975,"about_ca_system_score_gemma":0.00002320486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002268862,"about_ca_topic_score_gemma":0.000001655687,"domain_scores_codex":[0.9992585,0.00001484366,0.0001455487,0.0002431488,0.0001006791,0.0002372555],"domain_scores_gemma":[0.9993954,0.00009711727,0.00004908422,0.0003198835,0.00007140235,0.0000671166],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002509371,0.0006187572,0.00771532,0.0001183049,0.00007787701,0.0001582202,0.003534086,0.000004952135,0.03266529,0.6797286,0.04583939,0.2292883],"study_design_scores_gemma":[0.0005127333,0.001053908,0.005848196,0.00004260984,0.000008834988,0.00004071342,0.00007536421,0.01162432,0.8776841,0.07477008,0.02784729,0.000491882],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004481074,0.00007474355,0.9837203,0.0002019657,0.00007586514,0.000180567,0.000002491638,0.0005289894,0.01073398],"genre_scores_gemma":[0.5379956,0.00001063908,0.4611627,0.0003050832,0.00002485565,0.000006232693,9.618645e-7,0.000006262459,0.0004876956],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8450188,"threshold_uncertainty_score":0.3377844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07442839610254194,"score_gpt":0.3188149895157965,"score_spread":0.2443865934132545,"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."}}