{"id":"W6893034145","doi":"10.5281/zenodo.13870029","title":"T_tilde_CC_check","year":2024,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Perimeter Institute","funders":"","keywords":"Set (abstract data type); Identification (biology); Context (archaeology); Process (computing)","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":[0.001722948,0.003599074,0.002101574,0.003845967,0.001773673,0.003956649,0.003970012,0.003457371,0.1328344],"category_scores_gemma":[0.01258633,0.001223448,0.002773312,0.005999215,0.0009743465,0.002352945,0.002502464,0.00306806,0.1860578],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002050344,"about_ca_system_score_gemma":0.004354156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02729107,"about_ca_topic_score_gemma":0.06411994,"domain_scores_codex":[0.9977241,0.0003351827,0.0002091071,0.0008658891,0.0004974026,0.000368267],"domain_scores_gemma":[0.9950632,0.001596953,0.000267821,0.001718837,0.001062565,0.0002904889],"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":[0.00007385494,0.00001587449,0.0004854592,0.0003931198,0.00002710957,0.00001571152,0.00001204194,0.0002660433,0.000115063,0.0004289933,0.9967753,0.00139143],"study_design_scores_gemma":[0.0004197785,0.00003157496,0.002886106,0.0003023642,0.00006012757,0.00009627556,0.00008559709,0.001294033,0.001077631,0.003991126,0.989704,0.00005150219],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002016484,0.0001060679,0.0001877155,0.00008643446,0.00008266848,0.00001349988,0.9962998,0.001687006,0.001335049],"genre_scores_gemma":[0.0005407141,0.00004475633,0.0005304542,0.00006176751,0.00001301912,0.00004798852,0.9974692,0.0003711851,0.0009209824],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8671656,"threshold_uncertainty_score":0.4443755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03957819726819681,"score_gpt":0.2697105400760318,"score_spread":0.230132342807835,"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."}}