{"id":"W6910372846","doi":"10.48448/s69a-9373","title":"Bryan Hong, University of Toronto","year":2024,"lang":"en","type":"other","venue":"Underline Science Inc.","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Government (linguistics); Subject (documents); Agency (philosophy); Work (physics)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005641485,0.0002481492,0.0003085907,0.0003463815,0.0000760314,0.00005064864,0.001325379,0.0001727866,0.01111675],"category_scores_gemma":[0.00004593703,0.000251349,0.00008014429,0.0006926485,0.002095604,0.0002315531,0.0004563896,0.0001850043,0.01005833],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006403979,"about_ca_system_score_gemma":0.000808208,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02501113,"about_ca_topic_score_gemma":0.06739242,"domain_scores_codex":[0.9978101,0.00003029715,0.0001509381,0.0007290692,0.0008689283,0.0004107266],"domain_scores_gemma":[0.9987329,0.00001468095,0.0002135301,0.0007477144,0.0001105558,0.0001806542],"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":[0.000005186397,0.00006453178,0.00004276445,0.00008575786,0.00003593471,0.00003092389,0.000294465,0.00000440501,0.002169725,0.007586789,0.9869558,0.002723709],"study_design_scores_gemma":[0.000178416,0.00005951351,0.00006816316,0.0002992332,0.00007409041,0.000006037166,0.0007162833,0.0009585829,0.0001111313,0.0003795059,0.9968181,0.0003309595],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.00007705141,0.002452298,0.0002329769,0.00005493655,0.000718265,0.0002112436,0.0003525137,0.0005210157,0.9953797],"genre_scores_gemma":[0.01029347,0.0001228832,0.004597548,0.00002183752,0.0002068494,2.423077e-7,0.00002321795,0.0005014258,0.9842325],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.04238129,"threshold_uncertainty_score":0.9999939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01427135096259472,"score_gpt":0.2688049789037637,"score_spread":0.254533627941169,"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."}}