{"id":"W4398291406","doi":"10.7910/dvn/ii5jzg/hcc2q5","title":"MSP_F_100_NFL_4_22.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; History; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001290914,0.004806982,0.002623861,0.004855882,0.001303277,0.004578789,0.005901953,0.004641258,0.2665988],"category_scores_gemma":[0.009790226,0.001437837,0.002227242,0.008620774,0.0009949943,0.002899021,0.00312792,0.002613943,0.307332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002317773,"about_ca_system_score_gemma":0.002682371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01850849,"about_ca_topic_score_gemma":0.02646196,"domain_scores_codex":[0.9985903,0.0001972178,0.0001436041,0.0003881123,0.0003732208,0.0003075856],"domain_scores_gemma":[0.9970118,0.001052735,0.0002194002,0.0008029724,0.0006298048,0.0002832972],"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":[0.00003710984,0.00002584106,0.0002597697,0.0006848185,0.00001934939,0.00001172601,0.00001360199,0.0003561819,0.00005571431,0.0003641245,0.9967412,0.001430625],"study_design_scores_gemma":[0.00058685,0.0000431949,0.00189314,0.0004069023,0.00002937562,0.00008040854,0.00009041963,0.001135611,0.0006412436,0.002981432,0.992066,0.0000454018],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001176388,0.0000915057,0.00007454559,0.00007990804,0.0000307334,0.00001058271,0.9976436,0.0009653828,0.0009859393],"genre_scores_gemma":[0.0004891246,0.00009688842,0.0003684828,0.00007323697,0.00001393003,0.00008400201,0.9978922,0.0002381334,0.0007439348],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7334012,"threshold_uncertainty_score":0.8918617,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01407897303491542,"score_gpt":0.2087701466747898,"score_spread":0.1946911736398744,"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."}}