{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002886821,0.002144326,0.0008166214,0.0009417349,0.0003501831,0.0006467751,0.002583176,0.0009353096,0.00156087],"category_scores_gemma":[0.0006776746,0.0004498256,0.0007575715,0.0008709548,0.0003422789,0.001223717,0.0009352504,0.001135209,0.0009243102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009800872,"about_ca_system_score_gemma":0.001055296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01663177,"about_ca_topic_score_gemma":0.03086247,"domain_scores_codex":[0.9998029,0.00001520191,0.000009089536,0.00009370357,0.00004410863,0.00003504179],"domain_scores_gemma":[0.999811,0.00003512351,0.00003228326,0.00002922062,0.00007453666,0.00001793443],"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":[0.0003657567,0.0003732535,0.009494349,0.0003039116,0.0002314831,0.0003027168,0.00009156353,0.3658205,0.02083602,0.002830322,0.02507344,0.5742768],"study_design_scores_gemma":[0.000009589361,0.00004496983,0.000920078,0.00001501068,0.00001920161,0.00003967064,0.00001861373,0.9914339,0.004219162,0.001468291,0.00179988,0.00001175675],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1154527,0.001727437,0.8571334,0.0004166252,0.0002963165,0.0001750744,0.003980877,0.01586424,0.00495333],"genre_scores_gemma":[0.6391804,0.001239558,0.3221347,0.0007492189,0.0001344365,0.0003332069,0.0173099,0.0005178284,0.01840073],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01663177,"threshold_uncertainty_score":0.03306997,"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."}}