{"id":"W4300669942","doi":"10.54648/wtam2016014","title":"Annual Index","year":2016,"lang":"en","type":"article","venue":"World Trade and Arbitration Materials","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Government of Canada","funders":"","keywords":"Index (typography); Computer science; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002507635,0.0001329636,0.000166753,0.0001307174,0.0000766576,0.00006309105,0.00006400813,0.00003928284,0.001160601],"category_scores_gemma":[0.00001900407,0.00008787965,0.00001983544,0.0001243741,0.00007930868,0.0004435335,0.00001537896,0.00002640179,0.0006420367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001818082,"about_ca_system_score_gemma":0.00001966566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001465649,"about_ca_topic_score_gemma":0.00007431887,"domain_scores_codex":[0.9990663,0.00008991842,0.0002689806,0.0002258543,0.0001459439,0.0002030191],"domain_scores_gemma":[0.9996279,0.00004344854,0.00009225119,0.0001426642,0.0000140519,0.00007961092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007186913,0.00002419794,0.0009497606,0.000008358143,0.00001183183,0.000003573458,0.0001545523,1.739302e-7,0.9766235,0.01603576,0.005627106,0.0004892813],"study_design_scores_gemma":[0.001183686,0.00004199818,0.1168027,0.00006307309,0.00001999766,0.00001528663,0.0000510073,0.000001333504,0.8150669,0.004421595,0.06205001,0.0002823456],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9928581,0.00003739117,0.000100617,0.00277922,0.0002874841,0.0002110538,0.0008869772,0.0002028937,0.002636302],"genre_scores_gemma":[0.9979014,0.000009825389,0.0001200734,0.0006450922,0.0003045978,0.00002284543,0.00001549118,0.00003199286,0.0009486586],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1615566,"threshold_uncertainty_score":0.9997525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01417358216573403,"score_gpt":0.2431547188917632,"score_spread":0.2289811367260291,"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."}}