{"id":"W6947793616","doi":"10.48448/k5y2-h950","title":"Inferring friendships from mutual connections","year":2022,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Friendship; Mutual information; Pointwise mutual information; Mutual aid; Variation (astronomy); Affect (linguistics)","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0009641603,0.000475706,0.0004408155,0.002060335,0.0009618049,0.0002593849,0.001861081,0.000203632,0.09977396],"category_scores_gemma":[0.0007317397,0.0004978388,0.00009965939,0.002986432,0.001736023,0.0003295064,0.00109914,0.0009735634,0.0118577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000947546,"about_ca_system_score_gemma":0.001265917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004515546,"about_ca_topic_score_gemma":0.008297117,"domain_scores_codex":[0.9955069,0.0001281708,0.0004028729,0.001368178,0.001721917,0.0008719005],"domain_scores_gemma":[0.9976524,0.0002070673,0.0004177664,0.00126863,0.0001056944,0.0003485095],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000157069,0.0003360449,0.003833512,0.00001677311,0.0001343333,0.00007504337,0.0008970076,0.0004869299,0.004113691,0.0331099,0.9533215,0.003659567],"study_design_scores_gemma":[0.0004235897,0.00006835277,0.0003063161,0.00004934538,0.00006200508,0.00001668964,0.001095088,0.00402397,0.0001102235,0.001425601,0.9917258,0.0006930304],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001763185,0.0003550125,0.0007259294,0.0001755509,0.002596303,0.0004667306,0.002310659,0.002031489,0.9895751],"genre_scores_gemma":[0.2512217,0.00004889656,0.01975167,0.0009267828,0.003724088,0.0002710315,0.002269353,0.003909273,0.7178772],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.2716979,"threshold_uncertainty_score":0.9997473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0341852988178467,"score_gpt":0.2988702823990739,"score_spread":0.2646849835812272,"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."}}