{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.00190097,0.0002521996,0.000409654,0.00009556724,0.001573327,0.0003056229,0.003522357,0.0001195915,0.0000421821],"category_scores_gemma":[0.00123576,0.0001456212,0.0002427405,0.0001812014,0.0005148327,0.0002071456,0.0007371593,0.0001545293,0.000005336608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002592186,"about_ca_system_score_gemma":0.0008475953,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02113833,"about_ca_topic_score_gemma":0.4112507,"domain_scores_codex":[0.9977798,0.0002367135,0.0003333252,0.0006919233,0.00026897,0.0006892487],"domain_scores_gemma":[0.9936951,0.003910803,0.0002807435,0.001963225,0.00005817413,0.00009192125],"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":[0.00003158064,0.00003424238,0.00001353369,0.00006303375,0.0001865634,7.462939e-7,0.0001914394,5.39831e-7,2.020081e-7,0.002380837,0.9787161,0.01838122],"study_design_scores_gemma":[0.0001889775,0.00003084263,0.00005226626,0.0001562496,0.0004748124,3.738403e-7,0.0001754454,0.0002140123,8.275772e-8,0.001905849,0.996559,0.0002420715],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[1.037863e-7,0.000908614,0.002364495,0.001547077,0.0003710076,0.001261735,0.9908872,0.00003154136,0.002628205],"genre_scores_gemma":[0.000004432988,0.003194666,0.0005414468,0.0001079575,0.007460508,0.0003869929,0.9870367,0.00002298662,0.001244315],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3901124,"threshold_uncertainty_score":0.9997265,"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."}}