{"id":"W6908172019","doi":"10.25545/kbjhvd/lg1704","title":"Raw Data 4.rar","year":2024,"lang":"en","type":"dataset","venue":"UNB Dataverse","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"","keywords":"Raw material; Raw data; Product (mathematics); Production (economics); Data collection","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":[0.001763212,0.004942875,0.002356846,0.004914121,0.001378787,0.004599587,0.004038427,0.003696625,0.1630098],"category_scores_gemma":[0.009655208,0.001326178,0.002869083,0.006169545,0.0008973449,0.002665858,0.002827721,0.002866934,0.3043213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001586634,"about_ca_system_score_gemma":0.002629188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02023936,"about_ca_topic_score_gemma":0.02792923,"domain_scores_codex":[0.9978921,0.0004238661,0.0002217931,0.0006841383,0.0004279322,0.0003502542],"domain_scores_gemma":[0.9969946,0.0007130265,0.0001779224,0.001097071,0.0007239312,0.0002934359],"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":[0.00007317632,0.0000263522,0.0001598379,0.0004692383,0.00002331865,0.00001093726,0.00001641875,0.0001911545,0.0001477344,0.0002453082,0.9970639,0.001572579],"study_design_scores_gemma":[0.0004460473,0.00006408094,0.001564078,0.0003917617,0.00004697826,0.00005886323,0.0001052674,0.0009471778,0.001074215,0.001939087,0.9933044,0.00005794555],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001261205,0.0001054722,0.0001092383,0.00008435607,0.00007244744,0.00001962032,0.9960586,0.00244911,0.0009750487],"genre_scores_gemma":[0.0003473492,0.00005615994,0.000437098,0.00006185918,0.00001215442,0.00007152281,0.9977729,0.0004373605,0.0008035389],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8369902,"threshold_uncertainty_score":0.5453221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06167976807390602,"score_gpt":0.3247667692133602,"score_spread":0.2630870011394542,"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."}}