{"id":"W2053553533","doi":"10.1145/1286240.1286291","title":"Identifying subcommunities using cohesive subgroups in social hypertext","year":2007,"lang":"en","type":"article","venue":"","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Hyperlink; Hypertext; World Wide Web; Computer science; Sense of community; The Internet; Group (periodic table); Connection (principal bundle); Social network (sociolinguistics); Web page; Psychology; Social media; Mathematics; Social psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003680324,0.0001139349,0.0002006183,0.0001783505,0.0002121453,0.00005902602,0.0001774445,0.00002954812,0.0006478576],"category_scores_gemma":[0.000001288913,0.0001181,0.0001078037,0.0003273606,0.00005009753,0.0001380384,0.0001118494,0.0001720229,0.00001047448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005600421,"about_ca_system_score_gemma":0.00001550428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003115277,"about_ca_topic_score_gemma":0.0006291954,"domain_scores_codex":[0.9991635,0.00004988084,0.0002595895,0.0001232031,0.0001168198,0.0002869914],"domain_scores_gemma":[0.9996196,0.0000915774,0.00007034934,0.0001480962,0.00004242518,0.00002793184],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003042007,0.000263715,0.6850589,0.00001282538,0.000160461,0.00001333677,0.003846516,0.00005279094,0.00830764,0.2595666,0.001464563,0.04122224],"study_design_scores_gemma":[0.002939855,0.00009345235,0.417019,0.0002501663,0.0003839088,0.00001001333,0.08964867,0.02642108,0.06636195,0.3855811,0.008158354,0.003132388],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8734614,0.00003218281,0.08738429,0.00002128436,0.00002677366,0.00007899585,0.000001288807,0.00005936495,0.03893448],"genre_scores_gemma":[0.9955115,7.701712e-7,0.003881929,0.00004321968,0.0002880601,0.000003491622,0.00001407151,0.00001439954,0.0002425074],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2680399,"threshold_uncertainty_score":0.7093586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06131479344972673,"score_gpt":0.3369482575123982,"score_spread":0.2756334640626714,"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."}}