{"id":"W6889234799","doi":"10.25545/kbjhvd/5iit9u","title":"Raw Data 1.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":[0.001787145,0.004617861,0.002208747,0.005365932,0.001269949,0.004078003,0.003803949,0.003564727,0.1550763],"category_scores_gemma":[0.01113088,0.001215847,0.002555623,0.006419429,0.0009097635,0.002309662,0.002512318,0.002736738,0.299291],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001614431,"about_ca_system_score_gemma":0.003005816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02148917,"about_ca_topic_score_gemma":0.02996433,"domain_scores_codex":[0.9977134,0.0004718861,0.0002321585,0.0007276528,0.0005052572,0.0003496453],"domain_scores_gemma":[0.9966461,0.0008688138,0.0002043908,0.001109462,0.0008236959,0.0003475232],"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":[0.000066849,0.00002561198,0.0001713727,0.0004718186,0.00002098886,0.00001030581,0.00001464968,0.0002213094,0.0001362745,0.0002027173,0.9970387,0.001619363],"study_design_scores_gemma":[0.0004899009,0.00007329242,0.001872972,0.0004779469,0.00004896788,0.00006344352,0.0001075939,0.0009900283,0.0009573142,0.001753495,0.993105,0.00005996324],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001136831,0.0001040366,0.00008503966,0.00007233215,0.00005515844,0.00001909123,0.9974674,0.001330744,0.0007525799],"genre_scores_gemma":[0.00029632,0.00005557359,0.000387441,0.00005847476,0.000009993086,0.00007355588,0.9981568,0.0002715199,0.0006902335],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.8449237,"threshold_uncertainty_score":0.518782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06122037530603884,"score_gpt":0.3243999127778602,"score_spread":0.2631795374718213,"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."}}