{"id":"W1614987140","doi":"10.1111/j.1467-8462.2013.12010.x","title":"PanelWhiz and the Australian Longitudinal Data Infrastructure in Economics","year":2013,"lang":"en","type":"article","venue":"Australian Economic Review","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of Victoria","funders":"","keywords":"Longitudinal study; Longitudinal data; Indigenous; Software; Computer science; Data science; Data mining; Statistics","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":[],"consensus_categories":[],"category_scores_codex":[0.0227021,0.0006703802,0.001216486,0.004599794,0.0008850743,0.003464934,0.002812693,0.001033924,0.07868002],"category_scores_gemma":[0.07543737,0.001857454,0.001190307,0.01041221,0.0005953925,0.004620939,0.006434398,0.002580558,0.03062579],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002293851,"about_ca_system_score_gemma":0.008982776,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03735125,"about_ca_topic_score_gemma":0.02924733,"domain_scores_codex":[0.9920865,0.004156924,0.001002494,0.0008393323,0.001471526,0.0004431626],"domain_scores_gemma":[0.9515392,0.01914352,0.002971215,0.0170155,0.007046182,0.002284275],"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":[0.0008192814,0.0001825846,0.01177924,0.001418911,0.0002989112,0.000180078,0.001903563,0.003814541,0.000921085,0.06819975,0.7146205,0.1958616],"study_design_scores_gemma":[0.0002175254,0.0000811206,0.0245353,0.0004357834,0.0001097128,0.000143979,0.0002920783,0.01772319,0.001290637,0.04103682,0.9140018,0.0001319732],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01264685,0.00159266,0.4279498,0.006766091,0.0008484957,0.002778759,0.3253253,0.142348,0.07974412],"genre_scores_gemma":[0.08507509,0.002434189,0.5529307,0.003031755,0.0003790335,0.01141006,0.2687379,0.02379585,0.0522054],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.07868002,"threshold_uncertainty_score":0.2632108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1989587185493479,"score_gpt":0.3838557611003056,"score_spread":0.1848970425509577,"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."}}