{"id":"W2408481345","doi":"","title":"Extracting Data from the Deep Web with Global-as-View Mediators Using Rule-Enriched Semantic Annotations.","year":2014,"lang":"en","type":"article","venue":"","topic":"Web Data Mining and Analysis","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Computer science; Information retrieval; Semantic Web Stack; Deep Web; Semantic Web; Exploit; Social Semantic Web; Data Web; Semantic search; World Wide Web; Web page; Index (typography); Web search engine; Web modeling; Web crawler; Web search query; Search engine; The Internet","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":[0.0007333267,0.0001537564,0.0001978163,0.0000356237,0.0003175938,0.0004950958,0.002290573,0.000039426,0.00005402802],"category_scores_gemma":[0.0003963864,0.00009182181,0.00003430587,0.0008370468,0.00005238703,0.001033748,0.0006335562,0.0001248861,0.0001609496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002292198,"about_ca_system_score_gemma":0.0001243398,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001773602,"about_ca_topic_score_gemma":0.001047252,"domain_scores_codex":[0.9983512,0.0001772109,0.0002459461,0.0005768479,0.0003961024,0.0002526647],"domain_scores_gemma":[0.9970638,0.0006084597,0.0001650614,0.001999179,0.00007069775,0.00009276906],"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":[0.00002605231,0.0004241565,0.2467255,0.00008040191,0.00128467,0.0001039005,0.003299268,0.003826903,0.002809723,0.03919432,0.01257926,0.6896458],"study_design_scores_gemma":[0.0002011088,0.00001197037,0.004640634,0.00003732262,0.0001162927,0.00001539823,0.0003423734,0.9916127,0.00002130107,0.0008602857,0.001958108,0.0001825177],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1516526,0.0002637537,0.844285,0.001835074,0.0001383514,0.00006097467,0.00002489358,0.0001574779,0.001581862],"genre_scores_gemma":[0.8375628,0.0000216544,0.1612244,0.0009207911,0.0001316126,0.000001851832,0.0001032426,0.000007680824,0.00002598221],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9877858,"threshold_uncertainty_score":0.477422,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03602283158405946,"score_gpt":0.2909176166049133,"score_spread":0.2548947850208538,"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."}}