{"id":"W2124518790","doi":"10.2139/ssrn.397380","title":"Burning Out in Sequential Elimination Contests","year":2003,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Experimental Behavioral Economics Studies","field":"Social Sciences","cited_by":13,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Economics; Econometrics; Computer science; Environmental science","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":[0.008016679,0.0009305594,0.002420454,0.0008318131,0.001763793,0.003321276,0.001653058,0.003073144,0.02221923],"category_scores_gemma":[0.02505646,0.0008966847,0.0008834274,0.0005814207,0.002399429,0.004435898,0.002161503,0.002606121,0.001045171],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005957681,"about_ca_system_score_gemma":0.001262328,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004817724,"about_ca_topic_score_gemma":0.0008803923,"domain_scores_codex":[0.996537,0.001635255,0.0001700899,0.0003549636,0.0004436118,0.0008591057],"domain_scores_gemma":[0.9667896,0.02350601,0.003568467,0.003220003,0.0008794205,0.002036387],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.009764378,0.002151192,0.009925076,0.0008513604,0.0002908955,0.001005493,0.002445065,0.04330249,0.01163036,0.8159701,0.008606784,0.09405678],"study_design_scores_gemma":[0.0006593235,0.00114426,0.003035145,0.00004828995,0.0001016397,0.000459263,0.000762261,0.09963997,0.00172958,0.8892257,0.003140881,0.00005354578],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9023993,0.0001857057,0.05643976,0.001179264,0.0001404012,0.0001462045,0.00007227604,0.0001573587,0.03927973],"genre_scores_gemma":[0.9856473,0.0000580007,0.004906773,0.0001237188,0.00004908861,0.00004968856,0.0000321021,0.00003282216,0.009100359],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02221923,"threshold_uncertainty_score":0.07433075,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03082778519255722,"score_gpt":0.342150616509597,"score_spread":0.3113228313170398,"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."}}