{"id":"W7093307919","doi":"10.5281/zenodo.17420922","title":"Trained emulators for pybird in mnuw0waCDM","year":2025,"lang":"","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Twentieth Century Scientific Developments","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Set (abstract data type); Code (set theory); Artificial neural network; Training set; Key (lock)","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":["metaepi_narrow","sts","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002314464,0.0006840691,0.0007223574,0.001760021,0.006739632,0.005389971,0.003543248,0.0003457954,0.07928462],"category_scores_gemma":[0.001722331,0.0007776723,0.0002731564,0.0009330573,0.0007057125,0.0006204301,0.002773205,0.000932125,0.005963139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006880795,"about_ca_system_score_gemma":0.0000498117,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009418036,"about_ca_topic_score_gemma":0.00002237825,"domain_scores_codex":[0.9939921,0.0006240035,0.001322619,0.00170394,0.0009866285,0.001370657],"domain_scores_gemma":[0.9962942,0.000119727,0.0005033704,0.001339834,0.001367857,0.0003750211],"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":[0.0002220484,0.0005325307,7.263545e-7,0.0008594148,0.0001776097,0.00002069051,0.006743875,0.00001944388,0.00002827362,0.003628343,0.9591845,0.02858251],"study_design_scores_gemma":[0.002014585,0.0002676948,0.00008168629,0.0004564951,0.00008326021,0.00001218859,0.002777287,0.0001869467,0.00001299371,0.000274042,0.9930902,0.0007426752],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0008272357,0.0002894179,0.000339709,0.0008030172,0.01282333,0.003900736,0.9442796,0.0006378458,0.03609918],"genre_scores_gemma":[0.003747375,0.0003015187,0.0001407624,0.0003268583,0.0005148936,5.554352e-7,0.9367375,0.001601257,0.05662931],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07332148,"threshold_uncertainty_score":0.9994674,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04034038697198351,"score_gpt":0.2532641849287753,"score_spread":0.2129237979567918,"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."}}