{"id":"W4393639294","doi":"10.5281/zenodo.7009934","title":"Anticipated versus Actual Effects of Platform Design Change: A Case Study of Twitter's Character Limit","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Character (mathematics); Limit (mathematics); Computer science; Geography; Mathematics; Geometry; Mathematical analysis","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":[0.01282802,0.0005190025,0.0004154029,0.0009093852,0.003192676,0.002030792,0.001569855,0.002643483,0.003247818],"category_scores_gemma":[0.04746535,0.0004657197,0.0007296493,0.0009298628,0.00316419,0.003480575,0.002266897,0.002759459,0.0005931389],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003660817,"about_ca_system_score_gemma":0.001714075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009319345,"about_ca_topic_score_gemma":0.01584011,"domain_scores_codex":[0.9844586,0.01171282,0.0004771504,0.001084729,0.001469234,0.0007975319],"domain_scores_gemma":[0.935065,0.04930011,0.005713092,0.00515634,0.003328682,0.001436791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002924874,0.016579,0.3782557,0.001985914,0.0005310436,0.02021928,0.3626842,0.01935932,0.01504874,0.03362299,0.01368287,0.135106],"study_design_scores_gemma":[0.0008852387,0.0109933,0.2969484,0.001097097,0.0004810587,0.003423985,0.456415,0.06954084,0.03001333,0.02578624,0.1036724,0.0007431857],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.9901203,0.00006801512,0.002901362,0.001452585,0.00002850436,0.0002975953,0.0001675231,0.00003282115,0.004931328],"genre_scores_gemma":[0.9937503,0.00006841032,0.003707778,0.0003617899,0.00001703901,0.0004534485,0.00009493288,0.0000245191,0.001521836],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.01282802,"threshold_uncertainty_score":0.06784189,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1695811502361301,"score_gpt":0.3374737207128381,"score_spread":0.167892570476708,"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."}}