{"id":"W2991111171","doi":"10.1016/j.cviu.2020.103045","title":"Open cross-domain visual search","year":2020,"lang":"en","type":"preprint","venue":"Computer Vision and Image Understanding","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Visual search; Computer science; Domain (mathematical analysis); Sketch; Semantic space; Space (punctuation); Function (biology); Semantic search; Multidimensional scaling; Artificial intelligence; Information retrieval; Machine learning; Search engine; Algorithm; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002242703,0.00138713,0.002298969,0.001603663,0.001139595,0.002911064,0.003180939,0.003356599,0.01105276],"category_scores_gemma":[0.009125344,0.0005582468,0.001234716,0.002741102,0.001524982,0.009698927,0.007525011,0.001941427,0.003153526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008840031,"about_ca_system_score_gemma":0.001094341,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002796482,"about_ca_topic_score_gemma":0.002515682,"domain_scores_codex":[0.9974467,0.0006727463,0.0002164929,0.0008929238,0.0005072964,0.0002639405],"domain_scores_gemma":[0.9948053,0.00247245,0.0002258172,0.001780058,0.0004823343,0.0002340564],"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":[0.00185221,0.001260608,0.002655596,0.001854024,0.0002873144,0.0008439854,0.0006488704,0.08472987,0.02134126,0.04387571,0.02610757,0.814543],"study_design_scores_gemma":[0.0003905392,0.0009022466,0.002615325,0.0002323902,0.0001459281,0.003114431,0.001050086,0.7574438,0.0206644,0.1700297,0.0432778,0.0001335064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.116282,0.01120503,0.8403434,0.0006154444,0.0002214559,0.0004604773,0.001756364,0.00717111,0.02194479],"genre_scores_gemma":[0.6614223,0.001905243,0.321025,0.0005803696,0.0001544333,0.0002649979,0.005724457,0.0006370156,0.008286186],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01105276,"threshold_uncertainty_score":0.03697515,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1189654997218815,"score_gpt":0.424251377539317,"score_spread":0.3052858778174355,"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."}}