{"id":"W2618101958","doi":"10.1103/physrevd.74.071103","title":"Measurement of the inclusive jet cross section in<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"><mml:mi>p</mml:mi><mml:mover accent=\"true\"><mml:mi>p</mml:mi><mml:mo>¯</mml:mo></mml:mover></mml:math>interactions at<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\"><mml:msqrt><mml:mi>s</mml:mi></mml:msqrt><mml:mo>=</mml:mo><mml:mn>1.96</mml:mn><mml:mtext> </mml:mtext><mml:mtext> </mml:mtext><mml:mi>TeV</mml:mi></mml:math>using a cone-based jet algorithm","year":2006,"lang":"lv","type":"article","venue":"Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D, Particles, fields, gravitation, and cosmology","topic":"Particle physics theoretical and experimental studies","field":"Physics and Astronomy","cited_by":53,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institute of Particle Physics","funders":"Science and Technology Facilities Council","keywords":"Stress (linguistics); Section (typography); Jet (fluid); Mathematics; Artificial intelligence; Computer science; Physics; Speech recognition; Mechanics","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":[0.001485296,0.001128179,0.0005411132,0.001522602,0.001168354,0.002315192,0.001660508,0.001152439,0.01585481],"category_scores_gemma":[0.001700083,0.0006394683,0.0007954873,0.002501723,0.0004333377,0.0009700921,0.001055023,0.001485886,0.00436629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001051782,"about_ca_system_score_gemma":0.000844914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007758921,"about_ca_topic_score_gemma":0.01400338,"domain_scores_codex":[0.9980481,0.0002685773,0.00005132633,0.0004108868,0.00100041,0.0002208433],"domain_scores_gemma":[0.9984086,0.0005414463,0.0001728549,0.0002471377,0.000430989,0.0001990016],"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":[0.004780513,0.002920369,0.05481868,0.0007158591,0.0007763384,0.004022802,0.0009187491,0.07957073,0.4600792,0.06815971,0.1214806,0.2017564],"study_design_scores_gemma":[0.0005390809,0.001429284,0.0871067,0.00007906609,0.0001893182,0.001072861,0.0002094694,0.3867818,0.4885819,0.007443726,0.02623053,0.0003363209],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5810047,0.0007294499,0.2211544,0.001309314,0.0004880268,0.0005879935,0.02077055,0.01800002,0.1559555],"genre_scores_gemma":[0.8916344,0.0003909198,0.07265882,0.0003887487,0.00007044635,0.0002127348,0.01088905,0.002236979,0.02151791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01585481,"threshold_uncertainty_score":0.05303961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01869455110986324,"score_gpt":0.289792624438135,"score_spread":0.2710980733282717,"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."}}