{"id":"W4398296177","doi":"10.7910/dvn/ii5jzg/9r26jd","title":"MSP_F_70_NFL_4_26.xlsx","year":2020,"lang":"pt","type":"dataset","venue":"Harvard Dataverse","topic":"Optimization and Packing Problems","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Context (archaeology); Resolution (logic); Operations research; Computer science; History; Mathematics; Artificial intelligence; Archaeology","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":[0.0002570059,0.0009247953,0.0008679209,0.0002929747,0.0002095639,0.0004359243,0.001304513,0.0007406274,0.1074373],"category_scores_gemma":[0.0002734852,0.001076641,0.0002759754,0.0006270717,0.0001059517,0.000469652,0.000626374,0.001303536,0.8055872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001690019,"about_ca_system_score_gemma":0.0001618266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008317027,"about_ca_topic_score_gemma":0.00004587742,"domain_scores_codex":[0.9966761,0.0001336257,0.0008416693,0.0009559203,0.0006057333,0.0007868872],"domain_scores_gemma":[0.9969693,0.0001154632,0.0002583862,0.001932261,0.0001005647,0.0006240226],"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":[0.00003976792,0.00006908793,0.000008148569,0.0009796218,0.0003109152,0.0002246903,0.0001818462,0.0222836,0.00003072156,0.00014166,0.9753075,0.0004224233],"study_design_scores_gemma":[0.0009349733,0.00008052286,0.000006890372,0.0003140139,0.0003291045,0.0000281583,0.00009121359,0.02109049,0.00003378148,0.00001003155,0.9759868,0.001094045],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000001250033,0.00000619302,0.005279409,0.00003873747,0.003253569,0.0005490227,0.9877405,0.000534963,0.002596382],"genre_scores_gemma":[0.00005699307,0.002389148,0.002267714,0.000927796,0.0007959735,0.00003031211,0.9918056,0.0001845314,0.001541895],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.6981499,"threshold_uncertainty_score":0.9991684,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01977523660417536,"score_gpt":0.2253684998009558,"score_spread":0.2055932631967805,"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."}}