{"id":"W4392322222","doi":"10.1515/9781787445321-003","title":"List of Abbreviations","year":2019,"lang":"en","type":"book-chapter","venue":"Boydell and Brewer eBooks","topic":"Diverse Scientific and Economic Studies","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Privy Council Office","funders":"","keywords":"Computer science","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008937041,0.00173342,0.001431648,0.007927052,0.001837463,0.004228697,0.002303777,0.00152598,0.6962169],"category_scores_gemma":[0.005664741,0.0004702866,0.0006844942,0.007272831,0.0005719823,0.003594999,0.001881327,0.001954524,0.7101122],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002160612,"about_ca_system_score_gemma":0.003016099,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005047447,"about_ca_topic_score_gemma":0.005638151,"domain_scores_codex":[0.9989385,0.0001766389,0.00009506173,0.0002021088,0.0004706469,0.0001171442],"domain_scores_gemma":[0.9972708,0.0005522251,0.0001338577,0.0003192964,0.001403672,0.0003202103],"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":[0.00001159886,0.00001393473,0.00006240744,0.0001902963,0.000001961123,0.0000291927,0.00002911144,0.00008833856,0.0001584091,0.009244242,0.9420108,0.0481598],"study_design_scores_gemma":[0.000002514691,0.000006940556,0.00009341255,0.0001024621,0.000001902973,0.00003334872,0.0000252668,0.00003280002,0.00007927896,0.002241082,0.9973766,0.000004299871],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0004688447,0.007256357,0.008446052,0.003194195,0.01870081,0.0005745969,0.05732571,0.002928203,0.9011053],"genre_scores_gemma":[0.001791912,0.005788525,0.006612011,0.001296369,0.001914786,0.0005792819,0.04245671,0.001323984,0.9382364],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3037831,"threshold_uncertainty_score":0.4333096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04130003609767494,"score_gpt":0.1827912153264836,"score_spread":0.1414911792288087,"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."}}