{"id":"W4252613446","doi":"10.5334/kula.52","title":"Crowdsourcing Downunder","year":2019,"lang":"en","type":"article","venue":"KULA knowledge creation dissemination and preservation studies","topic":"Digital Humanities and Scholarship","field":"Arts and Humanities","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Crowdsourcing; Timeline; Outreach; Exhibition; Digitization; Data science; Process (computing); Citizen science; Computer science; Structuring; World Wide Web; Political science; History","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02124931,0.001052636,0.0007907219,0.004160088,0.01697646,0.01380573,0.003253319,0.003775062,0.04386429],"category_scores_gemma":[0.04331259,0.0007070636,0.001256859,0.003967009,0.01216367,0.01276685,0.02121308,0.005693639,0.01172089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00575999,"about_ca_system_score_gemma":0.007284035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02388448,"about_ca_topic_score_gemma":0.0199176,"domain_scores_codex":[0.9781357,0.008396875,0.0006071398,0.002904737,0.008363828,0.001591803],"domain_scores_gemma":[0.969545,0.01386586,0.001207988,0.008094363,0.005386332,0.001900448],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004659689,0.0002856853,0.005678673,0.0008957945,0.0001043279,0.001539227,0.1832813,0.003466037,0.00769334,0.3149464,0.1374236,0.3442198],"study_design_scores_gemma":[0.00003612233,0.00007607716,0.001537867,0.0003211639,0.00001804305,0.0001700529,0.02737248,0.002139685,0.00179432,0.03343423,0.933042,0.00005799518],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1123582,0.003361763,0.1246186,0.03563881,0.005252102,0.001772705,0.001115641,0.003087859,0.7127942],"genre_scores_gemma":[0.6183468,0.001825861,0.03311254,0.009405751,0.001290401,0.001520115,0.001285292,0.002881303,0.3303319],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04386429,"threshold_uncertainty_score":0.1467406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07623129909726001,"score_gpt":0.3347963506433833,"score_spread":0.2585650515461233,"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."}}