{"id":"W4398339189","doi":"10.7910/dvn/ii5jzg/hdtrk3","title":"MSP_F_100_NFL_4_42.xlsx","year":2020,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Materials science","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":[0.001184309,0.002439764,0.001519763,0.004597072,0.0008462427,0.003838117,0.003124718,0.002606906,0.3696568],"category_scores_gemma":[0.00947162,0.0009618028,0.001266113,0.008229708,0.0006116591,0.002438847,0.003016839,0.001748917,0.3654384],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001775992,"about_ca_system_score_gemma":0.002222409,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0266544,"about_ca_topic_score_gemma":0.03661836,"domain_scores_codex":[0.9986572,0.0001952551,0.0001627374,0.0003153448,0.0003137552,0.0003555618],"domain_scores_gemma":[0.9965851,0.0008442909,0.000358054,0.0007342924,0.001042682,0.0004354357],"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":[0.00002558987,0.0000102615,0.0003414084,0.0002737767,0.000009718306,0.000005901447,0.00001048032,0.00007408398,0.00003022767,0.0002613475,0.9980934,0.0008638523],"study_design_scores_gemma":[0.0003086897,0.00001962697,0.00347176,0.0003219832,0.00001511138,0.00003012433,0.00009189956,0.0002398646,0.0002326939,0.001121245,0.9941159,0.00003105762],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003961793,0.00001968707,0.00001901531,0.00005037722,0.00001723898,0.000004785057,0.9990563,0.000215155,0.0005778234],"genre_scores_gemma":[0.0002973573,0.00003843183,0.0001375688,0.00006769763,0.00001494457,0.00005778773,0.9979666,0.0001475967,0.001272129],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6303432,"threshold_uncertainty_score":0.8991081,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03145694927339215,"score_gpt":0.2131536742860324,"score_spread":0.1816967250126402,"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."}}