{"id":"W4310809250","doi":"10.18280/ts.390513","title":"Seedlings Supplement Device and Seedling Recognition Based on Convolution Neural Network","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Smart Agriculture and AI","field":"Agricultural and Biological Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Chinese Academy of Sciences","keywords":"Seedling; Artificial intelligence; Convolutional neural network; Transplanting; Test set; Computer science; Automation; Preprocessor; Set (abstract data type); Overfitting; Artificial neural network; Pattern recognition (psychology); Computer vision; Engineering; Horticulture; Mechanical engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001550087,0.0004343739,0.0003062589,0.0003213771,0.0001817407,0.0003321097,0.0006378198,0.0003889085,0.001881937],"category_scores_gemma":[0.0002268614,0.0002224518,0.0003600992,0.0003009415,0.0001875689,0.0006228478,0.0003300209,0.0003035927,0.0004589197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004816704,"about_ca_system_score_gemma":0.0005058079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005899905,"about_ca_topic_score_gemma":0.007223631,"domain_scores_codex":[0.9998527,0.000009328785,0.000006982675,0.00005716182,0.00005175475,0.00002203686],"domain_scores_gemma":[0.9999141,0.00001794006,0.00001413711,0.00001618787,0.00002679139,0.00001082614],"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":[0.0003343356,0.0001055794,0.007570396,0.0002477861,0.00005778046,0.0004568614,0.0001141208,0.05175136,0.4553235,0.003560114,0.004401394,0.4760767],"study_design_scores_gemma":[0.00001786024,0.0002490271,0.01154059,0.00001902903,0.00004370973,0.0003938345,0.00004043087,0.8773413,0.1028403,0.0008806527,0.006577138,0.00005601462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1730021,0.0005836512,0.8115717,0.0001663665,0.0001747266,0.0001465147,0.0004689888,0.006531894,0.007353987],"genre_scores_gemma":[0.808466,0.00038378,0.1793598,0.0001127553,0.00003172224,0.0001333569,0.000767292,0.00008380714,0.01066159],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005899905,"threshold_uncertainty_score":0.01173115,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02467946414700061,"score_gpt":0.2036619206428953,"score_spread":0.1789824564958947,"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."}}