{"id":"W4313824996","doi":"10.55537/cosie.v1i4.200","title":"Application of Weighted Product (WP) Method in Selection of Superior Seed Varieties of Sugar Cane","year":2022,"lang":"en","type":"article","venue":"Journal of Computer Science and Informatics Engineering (CoSIE)","topic":"Information Retrieval and Data Mining","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Saccharum officinarum; Production (economics); Cane; Selection (genetic algorithm); Sugar; Product (mathematics); Sugar production; Sugar cane; Raw material; Saccharum; Mathematics; Agricultural engineering; Agronomy; Biotechnology; Computer science; Biology; Engineering; Economics; Artificial intelligence; Food 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002216781,0.00008335901,0.0002669829,0.0007720946,0.00006768231,0.00004781049,0.0006421859,0.00001894791,0.00000172779],"category_scores_gemma":[0.00005850501,0.00007354806,0.0000356014,0.00164139,0.00005676479,0.001952848,0.0002830001,0.0001747651,1.251286e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006620598,"about_ca_system_score_gemma":0.0002373428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001814336,"about_ca_topic_score_gemma":3.22348e-7,"domain_scores_codex":[0.9980842,0.00001951053,0.0009992152,0.00006528089,0.0006887271,0.0001430663],"domain_scores_gemma":[0.9984434,0.00006582624,0.0007012773,0.0001692561,0.0005700884,0.00005010692],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007750727,0.0002038795,0.00450318,0.001196494,0.00006345669,0.000002122688,0.043911,0.7185189,0.0602588,0.04199223,0.0001167378,0.1291557],"study_design_scores_gemma":[0.0002518707,0.0002899544,0.006665415,0.00003857909,0.000004946692,0.00009858457,0.0001287805,0.9566696,0.03534773,0.00005720606,0.0003734462,0.00007382873],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.184334,0.00003061915,0.8152966,0.00003833715,0.0001805745,0.00008631311,0.000002700557,0.000007242463,0.00002363147],"genre_scores_gemma":[0.6737562,0.00001157313,0.3261912,0.00002106024,0.00001473999,0.000001595307,8.138896e-7,0.00000181619,9.914596e-7],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4894222,"threshold_uncertainty_score":0.2999203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0060550384007784,"score_gpt":0.2156049263952527,"score_spread":0.2095498879944743,"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."}}