{"id":"W6947832470","doi":"10.3886/e204941v1","title":"Promotion Incentives and GDP Manipulation","year":2024,"lang":"en","type":"dataset","venue":"ICPSR Data Holdings","topic":"Species Distribution and Climate Change","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Incentive; Promotion (chess); Code (set theory); Replication (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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002476973,0.0007133795,0.0007500777,0.002767204,0.0006904145,0.002093556,0.001663267,0.00107089,0.0401189],"category_scores_gemma":[0.01569548,0.0005815726,0.00078538,0.007226798,0.0004715772,0.001154094,0.001979843,0.002219096,0.0201817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001739142,"about_ca_system_score_gemma":0.003130687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06747502,"about_ca_topic_score_gemma":0.07158366,"domain_scores_codex":[0.9961954,0.0008892046,0.000345254,0.0007442211,0.001134482,0.0006914056],"domain_scores_gemma":[0.9910972,0.002211565,0.002964214,0.001352316,0.001821891,0.0005528138],"domain_codex":null,"domain_gemma":"incentives","domain_candidate":"incentives","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001860454,0.00007510287,0.01984651,0.0003266186,0.00006232936,0.00003328661,0.0001401866,0.0007274574,0.00007156791,0.003366067,0.9695669,0.005597897],"study_design_scores_gemma":[0.0003693238,0.00006695046,0.1174809,0.0003426571,0.00007117466,0.00008959774,0.0005876101,0.001170583,0.0005563109,0.002166942,0.8770211,0.00007687981],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.005347326,0.0001308623,0.0002300379,0.0005088755,0.00007470493,0.00005012497,0.9878411,0.0001757641,0.005641324],"genre_scores_gemma":[0.01767069,0.0001605045,0.00071708,0.0002657707,0.00005699414,0.0004604691,0.9725944,0.0001468432,0.007927218],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.997523,"threshold_uncertainty_score":0.1342111,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08036121535224329,"score_gpt":0.3044794752234275,"score_spread":0.2241182598711842,"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."}}