{"id":"W4398464864","doi":"10.7910/dvn/28075/c1lmzk","title":"changes.txt","year":2018,"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 (particle physics); Event data; 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":[0.004321847,0.001891638,0.001621403,0.007270349,0.001486308,0.007693062,0.002897231,0.001886267,0.4191506],"category_scores_gemma":[0.03296562,0.001471221,0.002144521,0.01236978,0.0009667819,0.005076597,0.003878302,0.003551416,0.3467732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002409988,"about_ca_system_score_gemma":0.004037397,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01223364,"about_ca_topic_score_gemma":0.01962583,"domain_scores_codex":[0.9957646,0.0006465505,0.0008697292,0.001129311,0.001078781,0.0005110193],"domain_scores_gemma":[0.9846111,0.004491086,0.00119961,0.004595543,0.003977919,0.001124786],"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":[0.00005822711,0.00001403421,0.0004467288,0.0002997228,0.00001917548,0.00001330505,0.00002717348,0.00004364912,0.00008546744,0.0006874601,0.9959098,0.002395191],"study_design_scores_gemma":[0.00009759523,0.000006881211,0.001705065,0.0001604588,0.00001477226,0.00002856179,0.00004955474,0.00006225109,0.0002266832,0.00151313,0.9961158,0.00001925275],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00006071245,0.00003449619,0.0001194007,0.000190141,0.0001825799,0.00003036322,0.9955757,0.001583951,0.002222722],"genre_scores_gemma":[0.0006001861,0.00008981948,0.0006271927,0.0002528803,0.00007643686,0.0001921767,0.9930855,0.001700304,0.003375517],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.5808494,"threshold_uncertainty_score":0.8285112,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1175330938547404,"score_gpt":0.3585129489778542,"score_spread":0.2409798551231139,"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."}}