{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01128449,0.001749355,0.001324185,0.0008253944,0.0006812114,0.001343398,0.001571119,0.001653464,0.0009174563],"category_scores_gemma":[0.02427087,0.0004439657,0.001589004,0.0006974885,0.0009436268,0.002907305,0.001158656,0.002705088,0.0003876285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002098207,"about_ca_system_score_gemma":0.001282746,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03141369,"about_ca_topic_score_gemma":0.01744085,"domain_scores_codex":[0.9977947,0.0007555328,0.0001702378,0.0008927372,0.0001965753,0.0001901989],"domain_scores_gemma":[0.9809132,0.01411559,0.001023408,0.002099322,0.001562066,0.0002863253],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0009092706,0.0004367569,0.04077054,0.0002855763,0.0006173103,0.0002329466,0.0004415142,0.8083054,0.005976285,0.001301871,0.003214354,0.1375082],"study_design_scores_gemma":[0.00001431986,0.0001999729,0.006355952,0.00003626572,0.00007613509,0.00007293942,0.0001301367,0.9895394,0.002024248,0.00109679,0.0004295955,0.00002425081],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8669847,0.003807922,0.1216321,0.001408956,0.0002212814,0.0002789239,0.0009420497,0.001293213,0.003430843],"genre_scores_gemma":[0.9763116,0.000648587,0.01934862,0.0003589264,0.00006213712,0.0001137086,0.001671629,0.0001441503,0.001340633],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03141369,"threshold_uncertainty_score":0.06246173,"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."}}