{"id":"W4398322454","doi":"10.7910/dvn/ii5jzg/zpv1rq","title":"MSP_F_50_NFL_4_3.xlsx","year":2020,"lang":"en","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; Geography; 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":[0.00007414162,0.0003277483,0.0003112827,0.0001202674,0.00005236841,0.0001227808,0.0005302767,0.0002715246,0.0228209],"category_scores_gemma":[0.00005958002,0.0003583606,0.00008595418,0.000207735,0.00003450263,0.000157746,0.000160078,0.0004926021,0.3902258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004967984,"about_ca_system_score_gemma":0.00003728275,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002322034,"about_ca_topic_score_gemma":0.00001986448,"domain_scores_codex":[0.9988781,0.00002675931,0.0002793253,0.0003010644,0.0002358571,0.0002788793],"domain_scores_gemma":[0.9988968,0.00003077159,0.00005417181,0.0007996458,0.00002749467,0.0001911647],"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":[0.000004328445,0.000009944176,7.200876e-7,0.0002895482,0.00006831934,0.0000485316,0.00001622376,0.007008225,0.000007697415,0.0000118502,0.9923654,0.000169198],"study_design_scores_gemma":[0.0002295309,0.00001500931,0.000001260456,0.00005172923,0.00006551969,0.000007594223,0.000007746283,0.002883809,0.00002160765,0.000006276283,0.9963204,0.0003894644],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[1.547631e-7,0.000002439266,0.0006141891,0.000005818287,0.001263671,0.0001727549,0.996171,0.0004429801,0.001327018],"genre_scores_gemma":[0.000002976145,0.001109747,0.0008367281,0.0003619125,0.0003234942,0.00001606447,0.9971122,0.00005586351,0.0001810106],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3674049,"threshold_uncertainty_score":0.9998868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01453785021965989,"score_gpt":0.2085162319873152,"score_spread":0.1939783817676553,"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."}}