{"id":"W2005177390","doi":"10.1080/10798587.2015.1015774","title":"Intelligent Information Technologies in Fruit Industry","year":2015,"lang":"en","type":"article","venue":"Intelligent Automation & Soft Computing","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Beijing; China; Chinese academy of sciences; Elite; Agriculture; Library science; Chinese society; Management; Political science; Computer science; Geography; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004544654,0.0001778562,0.0001824214,0.00006680124,0.0001047448,0.0001645228,0.0003219344,0.0003154693,0.00005865047],"category_scores_gemma":[0.0003269248,0.00007650802,0.00006388591,0.0008004929,0.00003760201,0.0005069798,0.0001715783,0.0003922009,0.0003513817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001333416,"about_ca_system_score_gemma":0.00001880357,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001229251,"about_ca_topic_score_gemma":0.0001558678,"domain_scores_codex":[0.9985766,0.00005309898,0.0005467088,0.0001984711,0.0003177017,0.0003073673],"domain_scores_gemma":[0.9993231,0.0001556707,0.0002041026,0.00006678709,0.0001736311,0.00007667967],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001255617,0.00009339279,0.04374268,0.00001069805,0.00001151155,0.000003848044,0.00195653,0.002931188,0.001473222,0.0007337472,0.003908455,0.9451222],"study_design_scores_gemma":[0.0007674874,0.000737772,0.2113018,0.0006838702,0.00003622094,0.00009708935,0.08480873,0.1182675,0.08690848,0.00922053,0.4851734,0.001997102],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930708,0.0001486391,0.001920984,0.002030704,0.000396915,0.0003059293,0.000005021042,0.0008469133,0.001274112],"genre_scores_gemma":[0.9990733,0.000009848872,0.0003738275,0.0002418155,0.0001524545,0.00001060501,0.00009001447,9.182475e-7,0.00004718093],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9431251,"threshold_uncertainty_score":0.4516419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03491421312981086,"score_gpt":0.2494221863959179,"score_spread":0.214507973266107,"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."}}