{"id":"W2094869475","doi":"10.1007/s11042-013-1496-7","title":"A novel specific image scenes detection method","year":2013,"lang":"en","type":"article","venue":"Multimedia Tools and Applications","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Scale-invariant feature transform; Computer science; Artificial intelligence; Classifier (UML); Computer vision; Computational complexity theory; Pattern recognition (psychology); Support vector machine; Image (mathematics); Feature selection; Feature (linguistics); Invariant (physics); Algorithm; Mathematics","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":[0.0001015377,0.0001080634,0.0001085075,0.00008136542,0.0001653172,0.0002836416,0.0002807104,0.00004791734,0.00002154543],"category_scores_gemma":[0.00002693337,0.00009533756,0.00003438567,0.000400386,0.00005314195,0.0009616326,0.0001208677,0.00009985658,0.000127318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001628839,"about_ca_system_score_gemma":0.00001031122,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002900552,"about_ca_topic_score_gemma":0.000001331362,"domain_scores_codex":[0.9992226,0.00001459488,0.0001605779,0.0003325797,0.0001010234,0.0001686313],"domain_scores_gemma":[0.9991888,0.0001601158,0.0000605794,0.0003823383,0.0001147989,0.0000933493],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[3.697792e-7,0.00002472412,0.000007222158,0.000003117779,0.000001783597,1.156063e-7,0.00002946707,4.07654e-7,0.268389,0.002431183,0.0001165366,0.728996],"study_design_scores_gemma":[0.0004330761,0.0000572311,0.008103775,0.00001381348,0.000007685808,0.00003830422,0.0000496068,0.05108641,0.7086195,0.02023405,0.2109462,0.0004102447],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001552908,0.0001946281,0.9974267,0.0003948229,0.00002770689,0.0006503289,0.000008344688,0.0003057074,0.0008364773],"genre_scores_gemma":[0.01704043,0.0002219392,0.9814382,0.0001342016,0.0001191748,0.0008697493,0.000005775063,0.000009393836,0.000161127],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7285858,"threshold_uncertainty_score":0.3887753,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02888891435787062,"score_gpt":0.3031350711603967,"score_spread":0.2742461568025261,"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."}}