{"id":"W4401727885","doi":"10.3390/agriculture14081412","title":"LettuceNet: A Novel Deep Learning Approach for Efficient Lettuce Localization and Counting","year":2024,"lang":"en","type":"article","venue":"Agriculture","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Shanghai Academy of Agricultural Sciences","keywords":"Artificial intelligence; Deep learning; Computer science; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001710083,0.0001270407,0.0001056793,0.00004535704,0.000184141,0.0003222532,0.0001878637,0.00007700087,5.935056e-7],"category_scores_gemma":[0.00006361676,0.00008553229,0.0000441485,0.000461832,0.00002075509,0.0003033169,0.00009754371,0.0001597546,0.000001770919],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003128606,"about_ca_system_score_gemma":0.00001068327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003854891,"about_ca_topic_score_gemma":5.007107e-7,"domain_scores_codex":[0.9991238,0.00001327856,0.0001259666,0.0003892446,0.0001502796,0.0001974437],"domain_scores_gemma":[0.9996494,0.00006400108,0.00003944345,0.0001028215,0.0001037766,0.00004055353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001850287,0.0002661004,0.0001930538,0.001203863,0.0001036881,0.0000244827,0.00463641,0.08958665,0.1993062,0.2042498,0.01046537,0.4899459],"study_design_scores_gemma":[0.0001308568,0.0000620545,0.0001435155,0.00007195194,0.00001315838,0.00004895939,0.0000694601,0.8992836,0.01272585,0.0003312274,0.08690075,0.0002185555],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0005001781,0.003150187,0.994791,0.000255412,0.00007286279,0.0002841179,0.000001652028,0.0005405627,0.0004040666],"genre_scores_gemma":[0.6099203,0.0001410314,0.3886398,0.0004172484,0.0002875397,0.00007994739,0.00004210154,0.00001992731,0.0004521113],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.809697,"threshold_uncertainty_score":0.3487906,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009758549558423977,"score_gpt":0.2417202834967483,"score_spread":0.2319617339383243,"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."}}