{"id":"W4393706976","doi":"10.5281/zenodo.3959240","title":"Dataset for \"Information Correspondence between Types of Documentation for APIs\"","year":2020,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Documentation; Computer science; Information retrieval; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002163057,0.00147298,0.0006676825,0.004646638,0.001003999,0.001523139,0.001270382,0.001413483,0.1143735],"category_scores_gemma":[0.01935757,0.0005449792,0.0008425348,0.003964938,0.000431628,0.001823827,0.002587379,0.001681494,0.0948205],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00177121,"about_ca_system_score_gemma":0.002447162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01003883,"about_ca_topic_score_gemma":0.01929462,"domain_scores_codex":[0.9968382,0.0007888345,0.0006994341,0.0006714308,0.0007478364,0.0002542109],"domain_scores_gemma":[0.9847978,0.00642225,0.00144619,0.002072541,0.004603231,0.0006579307],"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":[0.0001243362,0.00005335235,0.001714766,0.001202984,0.00001812458,0.00005470555,0.0002474737,0.0001450198,0.0005511201,0.0009773469,0.9871835,0.007727282],"study_design_scores_gemma":[0.0001586313,0.0000529987,0.01346671,0.0005316943,0.00002260715,0.000116448,0.0007129884,0.000502447,0.001306324,0.00165806,0.9814118,0.00005930905],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0007639005,0.00003511906,0.0004091655,0.0001189716,0.00005470414,0.00008235805,0.9964253,0.0004673109,0.001643061],"genre_scores_gemma":[0.001307584,0.00003015228,0.001131477,0.0001024675,0.00001816083,0.0005875601,0.9950611,0.000172531,0.001588867],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1143735,"threshold_uncertainty_score":0.3826175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1566688030143701,"score_gpt":0.3754561691232565,"score_spread":0.2187873661088863,"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."}}