{"id":"W4398742873","doi":"10.7910/dvn/ii5jzg/ga88mn","title":"MSP_F_70_NFL_4_19.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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0003031268,0.0003812555,0.0007856865,0.0002682537,0.000121842,0.0001917821,0.0009175879,0.0003160663,0.08929815],"category_scores_gemma":[0.00012506,0.0004462841,0.0002338456,0.0002736374,0.00007815434,0.000278078,0.0003652114,0.0005362573,0.6719782],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008652535,"about_ca_system_score_gemma":0.00005800221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008385563,"about_ca_topic_score_gemma":0.00008608671,"domain_scores_codex":[0.997876,0.00000709987,0.0008172785,0.000797861,0.00008695518,0.0004148049],"domain_scores_gemma":[0.9976769,0.00002297487,0.0006412438,0.001392112,0.0000291719,0.0002375864],"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":[0.00001445921,0.00004663596,0.0001332302,0.0001091442,0.0001011745,0.0000840077,0.0000142169,0.00002404829,1.522435e-7,0.002689289,0.9967344,0.00004928806],"study_design_scores_gemma":[0.0003318736,0.00004550445,0.0001562551,0.00002836333,0.00003913029,0.000006701694,0.0000095791,0.0006204973,0.000001018321,0.0004199557,0.9977968,0.0005443478],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00000921247,0.00001733238,0.00009243943,0.00004223461,0.001283633,0.0001782608,0.9955912,0.0000425324,0.00274317],"genre_scores_gemma":[0.00009565127,0.002644594,0.0001038257,0.001737158,0.0007596524,0.00001596848,0.9934595,0.00003921754,0.001144411],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.58268,"threshold_uncertainty_score":0.9997989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03265792087202939,"score_gpt":0.2127148499344653,"score_spread":0.1800569290624359,"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."}}