{"id":"W6908802530","doi":"10.25825/fk2/cfogzf/aaltid","title":"Syntax1.sps","year":2020,"lang":"en","type":"dataset","venue":"UNC Dataverse","topic":"German Social Sciences and History","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; University of Toronto","funders":"","keywords":"Process (computing); Identification (biology); Product (mathematics)","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":["insufficient_payload"],"category_scores_codex":[0.00053676,0.0001941823,0.0002835602,0.00008253651,0.001119491,0.0001511373,0.001387314,0.0003133046,0.01043367],"category_scores_gemma":[0.0003961353,0.0002032602,0.0001311492,0.0003846708,0.0007960353,0.000258308,0.000270425,0.0004006685,0.03896576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002924032,"about_ca_system_score_gemma":0.0007895945,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02136843,"about_ca_topic_score_gemma":0.03080059,"domain_scores_codex":[0.9977968,0.000193729,0.000209238,0.000476043,0.000866681,0.0004574943],"domain_scores_gemma":[0.9988236,0.000072141,0.0001883766,0.0004711655,0.00005454465,0.0003901795],"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":[0.000003579418,0.00002152473,0.000001114131,0.00001743409,0.00001092826,0.00005159166,0.00171759,1.542877e-7,4.042578e-7,0.001035839,0.9967831,0.0003567501],"study_design_scores_gemma":[0.000071824,0.00001831071,0.000005200923,0.00001723883,0.00005207738,3.516305e-7,0.002194371,4.942126e-7,1.510911e-7,0.0002005355,0.9971804,0.0002590219],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000001788032,0.00003118539,0.000002683666,0.0006133741,0.001841857,0.0001898628,0.9829012,0.00007267632,0.01434537],"genre_scores_gemma":[0.000008014096,0.0005369484,0.00007310845,0.001838058,0.001588073,0.000008369065,0.9927238,0.000009075229,0.003214609],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0285321,"threshold_uncertainty_score":0.9904709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03182286005621657,"score_gpt":0.3003454433513912,"score_spread":0.2685225832951746,"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."}}