{"id":"W4398547909","doi":"10.7910/dvn/28075/zotvww","title":"changes.txt","year":2015,"lang":"en","type":"dataset","venue":"Harvard Dataverse","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lockheed Martin (Canada)","funders":"","keywords":"Event data; Event (particle physics); Computer science; Database; Physics; Data modeling","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":[0.001731513,0.002607203,0.00188914,0.006067999,0.001074105,0.005859733,0.003579969,0.002098958,0.3168281],"category_scores_gemma":[0.01253262,0.001117954,0.001842063,0.0100697,0.0007126181,0.003788937,0.003748256,0.002695701,0.3541739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001914673,"about_ca_system_score_gemma":0.002585693,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01498682,"about_ca_topic_score_gemma":0.02269239,"domain_scores_codex":[0.9981111,0.0003109962,0.0002978987,0.0005611171,0.0003833133,0.0003354695],"domain_scores_gemma":[0.9950352,0.001167375,0.0004359181,0.001762877,0.001016496,0.0005821264],"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":[0.00004288709,0.00001066688,0.000287616,0.0003181606,0.00001788211,0.000008638156,0.00001477008,0.00006675085,0.0000427095,0.0004321279,0.9975326,0.001225121],"study_design_scores_gemma":[0.0001554568,0.00001183872,0.001462874,0.0002535233,0.00001446103,0.00002505175,0.00004943665,0.0001612516,0.0002106685,0.001472138,0.9961644,0.00001885112],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004168017,0.0000301551,0.00004169237,0.00007424102,0.00004320837,0.000009849628,0.9975296,0.0009620003,0.001267524],"genre_scores_gemma":[0.0003291986,0.00005708591,0.0001746381,0.00008248336,0.00001976169,0.00005796852,0.9976805,0.0004699439,0.001128391],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6831719,"threshold_uncertainty_score":0.9744617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1660605151595035,"score_gpt":0.3698224264556857,"score_spread":0.2037619112961822,"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."}}