{"id":"W7111571140","doi":"","title":"Alpesi anyakecskék vérmérsékletének kapcsolata a tejtermelésükkel egy hazai tenyészetben","year":2015,"lang":"hu","type":"article","venue":"Repository of the Academy's Library (Library of the Hungarian Academy of Sciences)","topic":"Global Trade and Competitiveness","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Work (physics); Production (economics)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","scholarly_communication","open_science"],"consensus_categories":[],"category_scores_codex":[0.001498549,0.001006631,0.001561696,0.0006443833,0.00120518,0.0006399704,0.01266081,0.0009876109,0.0002087815],"category_scores_gemma":[0.0002134593,0.0006867438,0.001216987,0.003564865,0.005514858,0.0151187,0.006882256,0.001826242,0.00005871667],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000426466,"about_ca_system_score_gemma":0.0007630073,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001102719,"about_ca_topic_score_gemma":4.491288e-7,"domain_scores_codex":[0.9913974,0.0007226996,0.002547616,0.001435076,0.002616391,0.001280803],"domain_scores_gemma":[0.9941635,0.0003694856,0.004148447,0.0009909058,0.00008045076,0.0002472167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001001775,0.001346987,0.6712028,0.003247701,0.0009413711,0.0000184672,0.0016543,0.001631813,0.03189112,0.1737906,0.1116615,0.001611476],"study_design_scores_gemma":[0.002813972,0.000240073,0.5748662,0.00547931,0.001014515,0.0001327619,0.001844507,0.003463062,0.1348209,0.05945858,0.2141488,0.001717381],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.689599,0.01412965,0.00001850695,0.1202122,0.004480386,0.002871691,0.0002165727,0.000412987,0.168059],"genre_scores_gemma":[0.9782615,0.0002427487,0.0006356984,0.006462868,0.001999007,0.00001784605,0.000007210102,0.0001295347,0.01224361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2886625,"threshold_uncertainty_score":0.9995584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03435659806152118,"score_gpt":0.2307112680819439,"score_spread":0.1963546700204227,"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."}}