{"id":"W7061691946","doi":"","title":"Recognizing Campaign Effects on Social Media","year":2020,"lang":"en","type":"other","venue":"OSF Preprints (OSF Preprints)","topic":"Magnetic confinement fusion research","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Social media; The Internet; Government (linguistics); Context (archaeology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001542874,0.0006841387,0.0005168321,0.008920198,0.001774164,0.002345504,0.0009614336,0.0006841955,0.01401142],"category_scores_gemma":[0.01074958,0.000309688,0.000337701,0.009620722,0.0003940439,0.001607662,0.001773586,0.0006593289,0.008260481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004020469,"about_ca_system_score_gemma":0.005106384,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.6478924,"about_ca_topic_score_gemma":0.8152366,"domain_scores_codex":[0.9971614,0.0002918539,0.0001420768,0.0003062333,0.001600276,0.0004982569],"domain_scores_gemma":[0.9876825,0.002394381,0.001258401,0.001474232,0.006220703,0.0009697226],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000463427,0.0002374967,0.2327474,0.001107024,0.0001750596,0.0002402571,0.002742329,0.000708802,0.00166602,0.002893305,0.6491011,0.1079177],"study_design_scores_gemma":[0.00003643382,0.00004980668,0.5844679,0.0004116145,0.0001004827,0.0001308657,0.002763001,0.002014896,0.0020208,0.0006921311,0.4072101,0.0001019709],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.07601969,0.0009314109,0.0006784415,0.0008434336,0.0002056914,0.0003942676,0.8785513,0.0007902633,0.04158546],"genre_scores_gemma":[0.170351,0.001063797,0.002435078,0.0003598216,0.0002362993,0.0007987226,0.7945264,0.0002838198,0.02994507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6478924,"threshold_uncertainty_score":0.7083626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01897034205174917,"score_gpt":0.2671698161163238,"score_spread":0.2481994740645747,"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."}}