{"id":"W6989531188","doi":"","title":"Binom Thue egyenletek kis megoldásainak kiszámítása","year":2014,"lang":"hu","type":"other","venue":"University of Debrecen Electronic Archive (University of Debrecen)","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Subject (documents); Government (linguistics); Quarter (Canadian coin)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003504073,0.0006033801,0.0008328973,0.001447932,0.001052511,0.002205447,0.001005793,0.0006078876,0.07835824],"category_scores_gemma":[0.01478991,0.0002951836,0.0004318171,0.001615057,0.0009211655,0.001860342,0.002462733,0.001321059,0.0123956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001563132,"about_ca_system_score_gemma":0.003163276,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005888025,"about_ca_topic_score_gemma":0.01127058,"domain_scores_codex":[0.9982746,0.0003020329,0.0002081513,0.0002195057,0.0007015323,0.0002942613],"domain_scores_gemma":[0.9944335,0.002157892,0.0006156011,0.0005835497,0.001584221,0.0006253032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.008421701,0.003487665,0.1644653,0.002922102,0.0005092356,0.0006980068,0.01000247,0.001103208,0.007225432,0.01367392,0.1467881,0.6407028],"study_design_scores_gemma":[0.0009675856,0.001396929,0.6727977,0.001446674,0.0005405614,0.0008583798,0.02448829,0.001856004,0.013008,0.02021789,0.262193,0.0002290615],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7576836,0.002571416,0.01467296,0.004524976,0.001334429,0.002350157,0.02434432,0.0005708053,0.1919474],"genre_scores_gemma":[0.8321717,0.002317958,0.01357257,0.001028024,0.0001646542,0.004568745,0.01631566,0.0004029478,0.1294577],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07835824,"threshold_uncertainty_score":0.2621344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05255048620711602,"score_gpt":0.2659230230667864,"score_spread":0.2133725368596704,"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."}}