{"id":"W2009788881","doi":"10.1117/12.478427","title":"&lt;title&gt;Recognition as Translating Images into Text&lt;/title&gt;","year":2003,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu; National Science Foundation","keywords":"Computer science; Feature (linguistics); Segmentation; Object (grammar); Artificial intelligence; Information retrieval; Image (mathematics); Cognitive neuroscience of visual object recognition; Natural language processing; Pattern recognition (psychology); Linguistics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003538061,0.0001553808,0.0001670647,0.00007047415,0.00006046186,0.0001139287,0.0006039557,0.000109272,0.00007920482],"category_scores_gemma":[0.0003056764,0.0001326907,0.0002636187,0.0002754801,0.00009945211,0.0004045616,0.00005583094,0.0001591384,0.00004055156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006761862,"about_ca_system_score_gemma":0.00003357526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001406353,"about_ca_topic_score_gemma":2.250216e-8,"domain_scores_codex":[0.9988582,2.411829e-8,0.0003182255,0.0002431977,0.0003879624,0.0001924583],"domain_scores_gemma":[0.9989479,0.0000519392,0.0001581853,0.0000503073,0.000731016,0.00006069983],"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":[0.000004227849,0.00003518684,0.000008088514,0.0001000668,0.00005047533,5.792857e-8,0.00008178748,6.995293e-7,0.3111194,0.6754047,0.005016831,0.008178431],"study_design_scores_gemma":[0.0005377687,0.0002173824,0.0001069115,0.0002623272,0.00007053429,0.00003059853,0.0002067138,0.01551293,0.8413325,0.04363268,0.09758931,0.0005003766],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5727413,0.0006445518,0.01961814,0.005223239,0.0007405077,0.0008536809,0.0000321553,0.0006369897,0.3995095],"genre_scores_gemma":[0.341022,0.0002530984,0.6553174,0.0002233098,0.0003560802,0.000115182,0.000008883381,0.00006410494,0.002639966],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6356993,"threshold_uncertainty_score":0.5410971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01305801403532724,"score_gpt":0.2373037049005581,"score_spread":0.2242456908652309,"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."}}