{"id":"W2894943649","doi":"10.5683/sp3/iddz4q","title":"Network Data for the Web Archives for Longitudinal Knowledge (WALK) Project","year":2016,"lang":"en","type":"dataset","venue":"Borealis","topic":"Data Analysis and Archiving","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Waterloo","funders":"","keywords":"World Wide Web; Longitudinal data; Computer science; Data science; Information retrieval; Data mining","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":[0.00342274,0.0012425,0.0006777046,0.005582879,0.0009878521,0.00226907,0.002169133,0.0009294075,0.03933174],"category_scores_gemma":[0.01782818,0.000692881,0.0008751917,0.007395551,0.000447212,0.001851095,0.003114615,0.002022664,0.03440671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001522058,"about_ca_system_score_gemma":0.004425177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02852343,"about_ca_topic_score_gemma":0.05124564,"domain_scores_codex":[0.9979529,0.0004585603,0.0004318091,0.0004151171,0.0005477424,0.0001938414],"domain_scores_gemma":[0.9909648,0.00186562,0.001144488,0.003143208,0.002012942,0.0008689713],"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":[0.0001016459,0.00003368239,0.003253595,0.0005863151,0.00005368283,0.00004693861,0.0001971254,0.0004738851,0.0003187536,0.003502582,0.984092,0.007339914],"study_design_scores_gemma":[0.0001145056,0.00001491436,0.008555195,0.0002940778,0.00004456539,0.0000828991,0.0002612543,0.0005234199,0.0008363235,0.005471565,0.9837586,0.0000425741],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0004484447,0.00002798601,0.0008344579,0.00008167708,0.00002552265,0.00004102691,0.996582,0.0008051911,0.001153776],"genre_scores_gemma":[0.0009402408,0.00003936097,0.001689792,0.00003498511,0.000007501949,0.0003015955,0.9957117,0.0002442747,0.001030613],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03933174,"threshold_uncertainty_score":0.1315777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1190118973990432,"score_gpt":0.3954184732304064,"score_spread":0.2764065758313632,"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."}}