{"id":"W4413015475","doi":"10.1038/s41598-025-14576-x","title":"Enhancing image retrieval through optimal barcode representation","year":2025,"lang":"en","type":"article","venue":"Scientific Reports","topic":"QR Code Applications and Technologies","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; Ontario Tech University; Brock University","funders":"","keywords":"Barcode; Computer science; Representation (politics); Information retrieval; Image (mathematics); Image retrieval; Artificial intelligence; Computer vision; Data science","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":[0.0006319809,0.0001033109,0.0001269995,0.0001924273,0.0004733519,0.00081712,0.0006075894,0.00006461601,0.00001691634],"category_scores_gemma":[0.0003556602,0.00009659387,0.00006947394,0.002013291,0.0002217244,0.0008694435,0.0005006177,0.0001145911,0.00003631411],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000053076,"about_ca_system_score_gemma":0.000172519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002243434,"about_ca_topic_score_gemma":0.000005669287,"domain_scores_codex":[0.9980361,0.00002338859,0.000409056,0.0009285785,0.0003419109,0.0002609683],"domain_scores_gemma":[0.99753,0.00005657829,0.0001662345,0.002001789,0.0002162719,0.00002916341],"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":[0.000004039208,0.00009384008,0.000568623,0.00002495761,0.00002218931,0.0002107175,0.0006749204,0.000118937,0.8249333,0.139847,0.02240048,0.01110104],"study_design_scores_gemma":[0.00005437229,0.000007987946,0.0004196144,0.00002101875,0.000005450304,0.00005242963,0.0001369497,0.002390489,0.8290688,0.1409568,0.02677153,0.0001145828],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1051478,0.0001430712,0.8805763,0.001551719,0.002514769,0.0002447354,4.180443e-7,0.0005937629,0.009227463],"genre_scores_gemma":[0.6778796,0.000007940045,0.3170163,0.00006336216,0.00001935357,0.00003178836,0.000008639769,0.000005032568,0.004968038],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5727319,"threshold_uncertainty_score":0.7879508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01617776825211567,"score_gpt":0.2945055333220268,"score_spread":0.2783277650699111,"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."}}