{"id":"W4391169863","doi":"10.3390/agriculture14020173","title":"Object Detection in Tomato Greenhouses: A Study on Model Generalization","year":2024,"lang":"en","type":"article","venue":"Agriculture","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Agriculture and Agri-Food Canada","keywords":"Generalization; Greenhouse; Object (grammar); Computer science; Biology; Horticulture; Artificial intelligence; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001241361,0.0002120945,0.0001637151,0.00002703365,0.0001226093,0.0001619327,0.0001549576,0.0001511526,0.00002731902],"category_scores_gemma":[0.00001533742,0.00006008743,0.00009721697,0.001080117,0.000008837943,0.0002033257,0.00003406835,0.0002106085,0.0001213913],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006025779,"about_ca_system_score_gemma":0.000005676869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003057732,"about_ca_topic_score_gemma":0.01159268,"domain_scores_codex":[0.9987599,0.00007134696,0.0002097004,0.0004456606,0.000268722,0.0002447035],"domain_scores_gemma":[0.9997559,0.00005187246,0.00003092798,0.00005155823,0.00004763501,0.00006205802],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005766965,0.001249869,0.009447164,0.00003350872,0.00007934762,0.0001451772,0.003268111,0.004844528,0.9044976,0.0007821521,0.02833321,0.0472617],"study_design_scores_gemma":[0.0004702845,0.001524536,0.9377989,0.000225325,0.00007990588,0.00005681884,0.004740164,0.008766593,0.02878858,0.0009680066,0.01557979,0.001001048],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971754,0.0002366633,0.0000185263,0.0006417962,0.0002335756,0.0004983354,0.00001436453,0.0005155341,0.000665813],"genre_scores_gemma":[0.9983991,0.00002150557,0.0000114684,0.0002577311,0.0005573413,0.00009129007,0.00005320521,0.000002252591,0.0006060485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9283518,"threshold_uncertainty_score":0.6468989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01553193535774591,"score_gpt":0.2247330981101018,"score_spread":0.2092011627523559,"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."}}