{"id":"W4398299432","doi":"10.7910/dvn/ii5jzg/xlv9os","title":"MSP_F_70_NFL_4_38.xlsx","year":2020,"lang":"tl","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); Computer science; Artificial intelligence; Geography; 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":[0.0002709706,0.0009189523,0.0008679542,0.0002825694,0.0002101044,0.0004554034,0.001349367,0.0007388006,0.1100828],"category_scores_gemma":[0.0003065668,0.001066846,0.0002726766,0.0006111699,0.0001187624,0.0004828351,0.0006516987,0.001303421,0.8105924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001723798,"about_ca_system_score_gemma":0.0001666578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009841019,"about_ca_topic_score_gemma":0.0000482313,"domain_scores_codex":[0.996655,0.000136953,0.0008444793,0.0009673173,0.0006055131,0.000790709],"domain_scores_gemma":[0.9968929,0.0001252051,0.0002607272,0.002001021,0.0001011689,0.0006189832],"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":[0.00004066102,0.00006820804,0.000008082659,0.0009496579,0.0003088976,0.0002322563,0.0001436772,0.02041896,0.00003353018,0.0001997865,0.9771524,0.0004438532],"study_design_scores_gemma":[0.0009413662,0.00007940444,0.00000785282,0.0003149413,0.0003298614,0.00002818271,0.0000697545,0.01704707,0.00003521416,0.00001736988,0.9800351,0.001093839],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000001584469,0.000006226415,0.003668359,0.00003943598,0.003261308,0.000546068,0.9880491,0.0005391607,0.003888778],"genre_scores_gemma":[0.00005017256,0.003019112,0.004132625,0.0009182665,0.0009958143,0.00002977796,0.9896566,0.0001782389,0.001019346],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7005097,"threshold_uncertainty_score":0.9991782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01787398217247222,"score_gpt":0.2187487614574297,"score_spread":0.2008747792849575,"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."}}