{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002994266,0.0001550833,0.0001919644,0.0001510359,0.0004261091,0.0005680078,0.00008714168,0.00003812188,0.001551355],"category_scores_gemma":[0.0002101499,0.0001344427,0.0000479372,0.00006664012,0.0001501591,0.001256497,0.00007740074,0.00008362086,0.0002029261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004264399,"about_ca_system_score_gemma":0.00001232375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001053523,"about_ca_topic_score_gemma":0.0001961189,"domain_scores_codex":[0.9991156,0.00004859071,0.0002743106,0.0002354736,0.0001744006,0.0001516233],"domain_scores_gemma":[0.9987401,0.0003601375,0.0001065415,0.0001473386,0.0006020839,0.00004380332],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002202627,0.00006821095,0.008118355,0.0001873681,0.00008375094,2.610624e-7,0.2070447,0.000004730549,0.0001038644,0.7602327,0.01388435,0.01024963],"study_design_scores_gemma":[0.0004026266,0.0000601445,0.01654157,0.0002123024,0.00003767158,0.00000130359,0.04653453,0.000585488,0.0001794431,0.01131371,0.9238235,0.0003076592],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3877051,0.001961354,0.00002010995,0.0005087092,0.0003505956,0.0002218419,0.000007630588,0.00009337955,0.6091312],"genre_scores_gemma":[0.6446986,0.00009345741,0.00001357507,0.00007130328,0.0001604369,0.00003039421,0.00005184251,0.00001063903,0.3548697],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9099392,"threshold_uncertainty_score":0.9993613,"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."}}