{"id":"W2561493008","doi":"","title":"Комплексная оценка качества мяса помесных свиней отечественной и канадской селекции","year":2013,"lang":"ru","type":"article","venue":"Все о мясе","topic":"Food Industry and Aquatic Biology","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Taste; Yield (engineering); Animal science; Biology; Food science; Computer science; Artificial intelligence","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":[0.000848291,0.0002984278,0.0002879062,0.001379239,0.001089278,0.002588365,0.0003580596,0.0005475557,0.008234992],"category_scores_gemma":[0.001657129,0.000401362,0.0002947036,0.001127195,0.001513459,0.0008333558,0.0007229573,0.001181528,0.002761177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001307776,"about_ca_system_score_gemma":0.002293796,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01005839,"about_ca_topic_score_gemma":0.01419516,"domain_scores_codex":[0.9991059,0.0001016564,0.00003791845,0.0001557504,0.0004823912,0.0001164531],"domain_scores_gemma":[0.9990262,0.0002319396,0.0001655607,0.0001139569,0.0003685803,0.00009374157],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003085705,0.0001223888,0.008806235,0.0007443553,0.00006681934,0.001277173,0.003517438,0.001899408,0.2148119,0.1719018,0.005687482,0.5908564],"study_design_scores_gemma":[0.00007295609,0.0004865541,0.04312687,0.0004005069,0.0002352759,0.004383274,0.003422861,0.003197972,0.1353978,0.07011235,0.7388511,0.0003123906],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3247687,0.07991941,0.1795371,0.006366887,0.002343669,0.0002639195,0.001039015,0.0006827715,0.4050786],"genre_scores_gemma":[0.8655492,0.0173836,0.06459804,0.0002788585,0.0004139121,0.000201439,0.0002120724,0.0001797154,0.05118321],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9899416,"threshold_uncertainty_score":0.02754873,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03170596688436562,"score_gpt":0.2068179464757936,"score_spread":0.175111979591428,"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."}}