{"id":"W4393487822","doi":"10.5281/zenodo.4276357","title":"Artifact for \"A Data-Centric Study of Software Tutorial Design\"","year":2020,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Artifact (error); Computer science; Software; Software engineering; Information retrieval; Data mining; Artificial intelligence; Programming language","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":["metaepi_narrow","sts","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001290115,0.0002658543,0.0004029248,0.0002968369,0.001301109,0.001120507,0.005776106,0.000119288,0.0005629884],"category_scores_gemma":[0.002465916,0.0002694293,0.00007166022,0.0006486266,0.00004427558,0.0004109732,0.004581675,0.0004602514,0.003006266],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001233204,"about_ca_system_score_gemma":0.0000207062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006573542,"about_ca_topic_score_gemma":3.43366e-7,"domain_scores_codex":[0.9966323,0.0007308195,0.0005476036,0.0009683037,0.0007098347,0.0004110841],"domain_scores_gemma":[0.9968401,0.0001532676,0.0004541747,0.001647176,0.0007358614,0.0001693859],"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":[0.00005861478,0.0002929808,2.770654e-7,0.0001774393,0.0001105433,0.00002065928,0.000549712,0.0002294504,0.00003514262,0.0004034316,0.9827079,0.01541389],"study_design_scores_gemma":[0.0005389004,0.001200181,0.000006064364,0.00006179154,0.00004232852,0.00001649696,0.0001179435,0.001258733,0.00003592901,0.0000215609,0.9964468,0.0002532896],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00001483616,0.00003890322,0.4841738,0.00005596785,0.0009015625,0.001536678,0.5127391,0.000467148,0.0000720383],"genre_scores_gemma":[0.0006625176,0.00003274177,0.003859238,0.00004148042,0.001037495,2.303352e-7,0.9932407,0.0007913081,0.0003343136],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.4805016,"threshold_uncertainty_score":0.999999,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1327285911634786,"score_gpt":0.2929648837340236,"score_spread":0.160236292570545,"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."}}