{"id":"W4398327611","doi":"10.7910/dvn/ii5jzg/b2wygf","title":"MSP_F_70_NFL_4_49.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); Operations research; Computer science; Mathematics; 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":[0.00007193783,0.0003324536,0.000319397,0.0001228111,0.00005173621,0.0001236678,0.00052629,0.0002825844,0.02594206],"category_scores_gemma":[0.00005908767,0.0003638647,0.00008840555,0.0002143379,0.00003327891,0.0001638921,0.0001506161,0.0004886263,0.3982313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005422069,"about_ca_system_score_gemma":0.00004095723,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002326614,"about_ca_topic_score_gemma":0.00002838131,"domain_scores_codex":[0.9988862,0.00002640239,0.000281744,0.0003034713,0.0002262803,0.0002759258],"domain_scores_gemma":[0.9988929,0.00003021258,0.00005682739,0.0008018386,0.00002794063,0.0001902841],"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":[0.000004519303,0.0000100652,8.986592e-7,0.0002804047,0.00007027554,0.00004948121,0.0000160272,0.007866835,0.000006143953,0.000009819609,0.9915318,0.0001536635],"study_design_scores_gemma":[0.0002429545,0.0000149811,0.000001458897,0.00005898374,0.00007056642,0.000007753582,0.00001133795,0.002974154,0.00001670529,0.000005808855,0.9961993,0.0003960579],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[2.518486e-7,0.000001986576,0.0006159569,0.000005635601,0.001336123,0.0001750226,0.9960622,0.0004622957,0.001340588],"genre_scores_gemma":[0.000003748159,0.0009628921,0.0009269616,0.0003683245,0.000344301,0.00001525014,0.9971733,0.0000568053,0.0001484038],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3722892,"threshold_uncertainty_score":0.9998813,"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."}}