{"id":"W4406753706","doi":"10.1016/j.ecoinf.2025.103015","title":"Beekeeping suitability prediction based on an adaptive neuro-fuzzy inference system and apiary level data","year":2025,"lang":"en","type":"article","venue":"Ecological Informatics","topic":"Insect and Arachnid Ecology and Behavior","field":"Biochemistry, Genetics and Molecular Biology","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre de Recherche en Sciences Animales de Deschambault; Université de Sherbrooke","funders":"Agriculture and Agri-Food Canada; Université de Sherbrooke; Ministère de l'Agriculture, des Pêcheries et de l'Alimentation","keywords":"Adaptive neuro fuzzy inference system; Apiary; Neuro-fuzzy; Inference system; Beekeeping; Computer science; Machine learning; Inference; Artificial intelligence; Fuzzy inference system; Fuzzy logic; Fuzzy control system; Ecology; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006829212,0.0004656015,0.0004410137,0.0006131302,0.0003661237,0.000867673,0.0005989085,0.0006120945,0.000814534],"category_scores_gemma":[0.001800283,0.0002169181,0.0004718267,0.0005416989,0.0002248589,0.000515847,0.0003109056,0.0004936454,0.000146602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007917899,"about_ca_system_score_gemma":0.0007327512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02333036,"about_ca_topic_score_gemma":0.01759159,"domain_scores_codex":[0.9997879,0.00004610957,0.00001977579,0.00006944639,0.00004642382,0.0000302735],"domain_scores_gemma":[0.999442,0.0003167737,0.00006288288,0.00003180741,0.0001285686,0.00001794592],"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":[0.0001353299,0.0001276661,0.01243897,0.00005846677,0.0000621071,0.0001196036,0.00007423761,0.9489316,0.002250235,0.0005535081,0.0003214406,0.03492675],"study_design_scores_gemma":[0.000001979761,0.00001569961,0.001144296,0.000002651751,0.000004871098,0.000004369706,0.00001026856,0.9984028,0.0002639111,0.0001143409,0.00003216363,0.000002719536],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7616051,0.000209448,0.2324293,0.0002970127,0.00005091112,0.0001225539,0.0004274815,0.0005494312,0.004308739],"genre_scores_gemma":[0.9878684,0.00003715226,0.01143847,0.00001833192,0.000004961402,0.00003761559,0.0001007634,0.000003849159,0.0004905313],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02333036,"threshold_uncertainty_score":0.0463891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05327820102511319,"score_gpt":0.2951210206724837,"score_spread":0.2418428196473705,"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."}}