{"id":"W4408860927","doi":"10.1109/icce63647.2025.10929932","title":"Copula Based Bird Cherry Plant Identification","year":2025,"lang":"en","type":"article","venue":"","topic":"Date Palm Research Studies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Northern British Columbia","funders":"","keywords":"Copula (linguistics); Computer science; Identification (biology); Econometrics; Artificial intelligence; Machine learning; Mathematics; Biology; Ecology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006223767,0.0008844542,0.001105958,0.00140667,0.0004320907,0.0006096637,0.0009635707,0.0008336214,0.002402068],"category_scores_gemma":[0.0009555271,0.0002272405,0.001109747,0.0008105132,0.0002764334,0.0008554987,0.0007209885,0.0008559631,0.001914493],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004590785,"about_ca_system_score_gemma":0.0006099746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005556437,"about_ca_topic_score_gemma":0.01019234,"domain_scores_codex":[0.9995819,0.00005595452,0.00001635814,0.0001723535,0.00009094128,0.00008260059],"domain_scores_gemma":[0.9994444,0.0001345931,0.00005947534,0.00008363269,0.0002311923,0.00004670116],"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":[0.0006183551,0.0003498713,0.0160176,0.0001789673,0.0003905276,0.0006206936,0.0001616319,0.08089797,0.08201519,0.001934483,0.01765942,0.7991552],"study_design_scores_gemma":[0.00001090753,0.0000543798,0.006099887,0.000009669544,0.00004247744,0.0002810943,0.00004777716,0.97837,0.0123865,0.0006388095,0.002037749,0.0000206585],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2138746,0.001782542,0.7709021,0.0003503294,0.0001784022,0.0001506566,0.00109656,0.005433157,0.006231686],"genre_scores_gemma":[0.6919909,0.0006130953,0.2931612,0.0003995377,0.0001513912,0.00008696759,0.003648854,0.0004283803,0.009519703],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005556437,"threshold_uncertainty_score":0.0110482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0254366227265142,"score_gpt":0.27013338476876,"score_spread":0.2446967620422458,"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."}}