{"id":"W2395243874","doi":"","title":"LILAC - Learn from Internet: Log, Annotation, and Content.","year":2009,"lang":"en","type":"article","venue":"","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Institute for Nanotechnology","funders":"","keywords":"Computer science; The Internet; lilac; World Wide Web; Annotation; Information retrieval; Artificial intelligence","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002391585,0.00128931,0.0006507625,0.002924679,0.00056353,0.001401601,0.001819028,0.001112133,0.008461942],"category_scores_gemma":[0.01479043,0.0005318522,0.0005338544,0.002342309,0.0003421298,0.003346932,0.001636966,0.001726409,0.01084971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001010706,"about_ca_system_score_gemma":0.001601885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01352348,"about_ca_topic_score_gemma":0.03073334,"domain_scores_codex":[0.9986584,0.0004075281,0.0001055912,0.000292677,0.0004566135,0.00007924779],"domain_scores_gemma":[0.9907892,0.004648423,0.0003453385,0.00255004,0.001251158,0.0004157918],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002158785,0.003274094,0.05359798,0.0008791263,0.0003114665,0.0003206858,0.000523105,0.03269272,0.003710799,0.002110098,0.2106985,0.6897227],"study_design_scores_gemma":[0.0002449636,0.0008825064,0.02658691,0.0001406331,0.0001431291,0.0004199734,0.0004707087,0.8734183,0.0195476,0.006503897,0.07149773,0.0001437372],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"software","genre_gemma":"software","genre_scores_codex":[0.2952698,0.001562289,0.1985902,0.001631483,0.0004400909,0.002039747,0.1163595,0.3497759,0.03433086],"genre_scores_gemma":[0.5241995,0.0007948599,0.1991659,0.0005085851,0.0001274899,0.001745544,0.2445746,0.004341838,0.02454154],"genre_candidate":"software","genre_consensus":"software","teacher_disagreement_score":0.01352348,"threshold_uncertainty_score":0.02830803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03929942071561514,"score_gpt":0.2466718810140445,"score_spread":0.2073724602984294,"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."}}