{"id":"W4398446281","doi":"10.7910/dvn/ii5jzg/wkzwxu","title":"MSP_F_50_NFL_4_2.xlsx","year":2020,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Data Analysis with R","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Physics","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002436088,0.003373422,0.00288135,0.004759792,0.001658386,0.004469438,0.005299791,0.003283463,0.4920615],"category_scores_gemma":[0.01499148,0.00158412,0.001879217,0.006637097,0.001132706,0.002922629,0.003950389,0.002319511,0.3912707],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001688254,"about_ca_system_score_gemma":0.003359416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01161245,"about_ca_topic_score_gemma":0.01654055,"domain_scores_codex":[0.9981459,0.0003261925,0.0002163381,0.0005205257,0.0003691414,0.0004218851],"domain_scores_gemma":[0.9947932,0.002123311,0.0004024596,0.00108274,0.001105031,0.0004931776],"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":[0.00006520298,0.00001892507,0.0002886993,0.0008055567,0.00002538502,0.00001544428,0.00003216093,0.000111725,0.0001304259,0.0004374418,0.9967265,0.001342414],"study_design_scores_gemma":[0.0007593873,0.00003743293,0.002098167,0.0004962739,0.00004682706,0.00006922815,0.00008827006,0.0003270984,0.000936067,0.002933074,0.9921311,0.0000772038],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00007423381,0.00003542819,0.0001391324,0.00006922882,0.00002997847,0.00002213912,0.9971853,0.001572224,0.0008723617],"genre_scores_gemma":[0.0008169636,0.00007623353,0.001145466,0.0001866074,0.00002636154,0.0005537807,0.9937761,0.001686202,0.001732246],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5079385,"threshold_uncertainty_score":0.7245127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01701250023279021,"score_gpt":0.237103589966024,"score_spread":0.2200910897332338,"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."}}