{"id":"W2674238586","doi":"10.1145/3091107","title":"Search by Screenshots for Universal Article Clipping in Mobile Apps","year":2017,"lang":"en","type":"article","venue":"ACM Transactions on Information Systems","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Information retrieval; Chunking (psychology); Usability; Clipping (morphology); Rank (graph theory); Learning to rank; Key (lock); Artificial intelligence; Human–computer interaction; Ranking (information retrieval)","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":[0.0005010613,0.00008099616,0.0001282001,0.0002218148,0.000524865,0.0006927365,0.0009355164,0.00005629516,0.000004669391],"category_scores_gemma":[0.00003292443,0.00008045111,0.00005673344,0.0001828622,0.00002813958,0.003523828,0.00001771665,0.0001060109,0.00009933066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006341322,"about_ca_system_score_gemma":0.00003960895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004032084,"about_ca_topic_score_gemma":0.00001924679,"domain_scores_codex":[0.9991174,0.00003386823,0.0002997166,0.0001360189,0.000214832,0.0001981623],"domain_scores_gemma":[0.9986514,0.00009870183,0.0001204563,0.0009742468,0.00009182572,0.00006338304],"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":[0.00008858674,0.0002439485,0.002611367,0.0002543051,0.0001757349,0.000003149699,0.01023192,0.1398628,0.0009845257,0.006950509,0.004392464,0.8342007],"study_design_scores_gemma":[0.001799112,0.0001776613,0.001024795,0.0001650862,0.00001796699,0.000008129167,0.005009876,0.9264542,0.00329212,0.00004728915,0.06166667,0.0003370752],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02755338,0.00001401917,0.9707691,0.0002997727,0.0001957676,0.000275869,0.00008315694,0.00007034206,0.0007386045],"genre_scores_gemma":[0.9956838,0.0000145197,0.003929389,0.00004522194,0.00001535829,0.00008483375,0.0000222754,0.000003405437,0.0002011591],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9681305,"threshold_uncertainty_score":0.6680074,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02974584000255332,"score_gpt":0.2824627019365408,"score_spread":0.2527168619339875,"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."}}