{"id":"W4411577925","doi":"10.1016/j.ecoinf.2025.103296","title":"Urban buzz or urban bust? Beekeeping challenges, suitability, and survival insights in Montreal, Canada","year":2025,"lang":"en","type":"article","venue":"Ecological Informatics","topic":"Plant and animal studies","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada); Université de Montréal","funders":"Institut de Valorisation des Données; Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Marketing buzz; Bust; Beekeeping; Geography; Boom; Ecology; Environmental science; Biology; Business; Advertising; Environmental engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.00056769,0.0003721121,0.0002405837,0.0009313293,0.002243444,0.001362044,0.001174315,0.0002798044,0.00347701],"category_scores_gemma":[0.001395929,0.0001535287,0.000353035,0.00191662,0.000904339,0.0004217385,0.000715835,0.0004775144,0.0002188798],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.03565213,"about_ca_system_score_gemma":0.02505056,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9974124,"about_ca_topic_score_gemma":0.9991459,"domain_scores_codex":[0.9997397,0.00003333096,0.000005062287,0.00004376035,0.00004982157,0.0001283986],"domain_scores_gemma":[0.9993148,0.00008601511,0.00008411293,0.0000251967,0.0003009636,0.0001889944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001916444,0.00009943137,0.8957931,0.0001593944,0.0001281059,0.0006721843,0.005913985,0.008748363,0.001062039,0.003806421,0.01575457,0.06767067],"study_design_scores_gemma":[0.00001219544,0.00005318535,0.9558942,0.0001303132,0.00004679663,0.00008663829,0.01341777,0.01163398,0.0002120177,0.0004842754,0.01797208,0.00005662653],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9814842,0.001852014,0.001358013,0.001888387,0.00003627881,0.00007832255,0.005352737,0.00007201277,0.007878012],"genre_scores_gemma":[0.9918236,0.0009261109,0.00104032,0.0001648855,0.00001047722,0.00002594162,0.00165493,0.00002093449,0.004332723],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03565213,"threshold_uncertainty_score":0.2586755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04507239562007757,"score_gpt":0.2147254663885043,"score_spread":0.1696530707684267,"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."}}