{"id":"W6982280025","doi":"","title":"Heterogeneity in focus : creating and using linguistic databases","year":2006,"lang":"en","type":"other","venue":"publish.UP (University of Potsdam)","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Atomic Energy of Canada Limited","keywords":"Focus (optics); Annotation; Point (geometry); Structuring; Work (physics); Deep linguistic processing","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006624977,0.0004605748,0.0008173613,0.001830955,0.0001709831,0.0001147531,0.0006064374,0.0003316568,0.0006184428],"category_scores_gemma":[0.0004435128,0.0006458714,0.0001417243,0.0008454848,0.0004309652,0.0005505319,0.0006959559,0.0004094462,0.00005770024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002994404,"about_ca_system_score_gemma":0.0002181066,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1586435,"about_ca_topic_score_gemma":0.1325679,"domain_scores_codex":[0.9975086,0.0003089861,0.0002588078,0.000855362,0.0004903061,0.0005779496],"domain_scores_gemma":[0.9980754,0.0001275131,0.0007404651,0.0007097186,0.0001519406,0.0001949263],"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":[0.0002539941,0.0009005272,0.199721,0.002423883,0.0009849733,0.001467337,0.00345842,0.0003072303,0.001920787,0.002931082,0.7748384,0.01079234],"study_design_scores_gemma":[0.00830083,0.0001321623,0.07896233,0.003919268,0.001336974,0.0001046318,0.003914426,0.007477595,0.0001204222,0.0003794272,0.8917479,0.003604047],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.09251257,0.003805987,0.002280294,0.00005166918,0.0005788805,0.0009652873,0.005861076,0.0006682243,0.893276],"genre_scores_gemma":[0.485388,0.0002512928,0.04613341,0.00003966321,0.001695952,0.000001444932,0.003204254,0.003033204,0.4602528],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.4330232,"threshold_uncertainty_score":0.9995993,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03826709034432211,"score_gpt":0.2534231369593912,"score_spread":0.2151560466150691,"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."}}