{"id":"W2157940332","doi":"10.1145/1718487.1718525","title":"Early online identification of attention gathering items in social media","year":2010,"lang":"en","type":"article","venue":"","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Social media; Computer science; Identification (biology); Set (abstract data type); Task (project management); World Wide Web; Internet privacy; Information retrieval; Data science","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":[0.0001395202,0.00004660591,0.0000965422,0.00006960376,0.00002302031,0.00001428737,0.00008427996,0.00002125306,0.0002508021],"category_scores_gemma":[0.000003048979,0.00004652007,0.000060952,0.0001660544,0.00002033369,0.00006680971,0.00002304709,0.0001000142,0.00000501948],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004437312,"about_ca_system_score_gemma":0.000005573068,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002780755,"about_ca_topic_score_gemma":0.0003630327,"domain_scores_codex":[0.9995035,0.00001329695,0.0002386814,0.0000925406,0.00007909047,0.00007286954],"domain_scores_gemma":[0.9997222,0.00002391201,0.0001015424,0.0001011699,0.00003944079,0.00001173047],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.000003711156,0.0002172408,0.3409856,0.000003655534,0.00002457096,9.793563e-8,0.0004792026,0.000005438808,0.5510712,0.05284503,0.0001589109,0.05420531],"study_design_scores_gemma":[0.0001446388,0.000004032159,0.9706625,0.000005551729,0.00001398849,3.45377e-8,0.0001827885,0.00322566,0.009356335,0.0161473,0.0001704301,0.00008673732],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9911649,0.000001359172,0.007493951,0.00003490138,0.00004411958,0.00004759916,0.000005292277,0.00002344929,0.001184384],"genre_scores_gemma":[0.9987785,2.528792e-7,0.0007387546,0.0000014555,0.0003027119,0.000006273961,0.00005189774,0.000005679763,0.0001145173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6296769,"threshold_uncertainty_score":0.2746107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01479545160627812,"score_gpt":0.2799124049652795,"score_spread":0.2651169533590014,"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."}}