{"id":"W2168616308","doi":"10.1109/tkde.2008.34","title":"Automatic Website Summarization by Image Content: A Case Study with Logo and Trademark Images","year":2008,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Killam Trusts","keywords":"Automatic summarization; Computer science; Trademark; Logo (programming language); Information retrieval; Logos Bible Software; World Wide Web; Web page; Image (mathematics); Trademark infringement; Artificial intelligence; Process (computing); Abstraction","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000138534,0.0001616781,0.0001580884,0.0001313117,0.0002205963,0.0001088128,0.0002454754,0.00003923165,0.000003978493],"category_scores_gemma":[0.000005199617,0.0001373087,0.00001376964,0.0003064011,0.00005367822,0.001011827,0.0000105706,0.0001454052,0.000004277105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001757002,"about_ca_system_score_gemma":0.00002295022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004239965,"about_ca_topic_score_gemma":0.00001228203,"domain_scores_codex":[0.9991489,0.00003120725,0.0001790949,0.0003892122,0.0001053456,0.0001462062],"domain_scores_gemma":[0.9992323,0.00008682307,0.0000336629,0.0005062779,0.00005112214,0.00008984849],"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":[0.0001583784,0.007244309,0.002931041,0.001525025,0.0009818659,0.005280012,0.018605,0.0002505688,0.1492615,0.0004029055,0.005437952,0.8079215],"study_design_scores_gemma":[0.001820015,0.000882222,0.001634473,0.0001360011,0.0001255239,0.005517611,0.0006432281,0.9326578,0.05500207,0.000005880363,0.0008290706,0.000746111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06050267,0.0003335742,0.9383507,0.00004707485,0.00005243124,0.0002760217,0.00005976743,0.0003519682,0.00002580536],"genre_scores_gemma":[0.9857627,0.000215358,0.01380363,0.00001133946,0.00001069884,0.00003315755,0.000009890039,0.00001431882,0.0001389344],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9324072,"threshold_uncertainty_score":0.5599288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03497416171297994,"score_gpt":0.2577001686089124,"score_spread":0.2227260068959325,"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."}}