{"id":"W4400485376","doi":"10.61091/jcmcc120-14","title":"An Image Identification System for Agricultural Items Based on Machine Vision","year":2024,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Remote Sensing and Land Use","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Identification (biology); Machine vision; Computer vision; Artificial intelligence; Agriculture; Computer science; Image (mathematics); Geography; Biology; Botany","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":[0.0003525003,0.0004816421,0.0006020869,0.001222697,0.0004768737,0.0006991489,0.0007199181,0.0005965789,0.003797543],"category_scores_gemma":[0.0004742795,0.0002369245,0.0004259853,0.0007703493,0.0001646484,0.0008922959,0.0004084463,0.0004391696,0.003268297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005394742,"about_ca_system_score_gemma":0.0004471965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002979912,"about_ca_topic_score_gemma":0.004516496,"domain_scores_codex":[0.9997149,0.00002637933,0.00001200183,0.0001057195,0.0001094724,0.00003139862],"domain_scores_gemma":[0.9997495,0.00003735327,0.00002916178,0.00005112632,0.0001152004,0.00001754406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0003299302,0.0003894793,0.005922708,0.0001864281,0.00008831153,0.0002142107,0.0001192287,0.01866146,0.1567039,0.002110519,0.00891661,0.8063573],"study_design_scores_gemma":[0.00003429035,0.0003060107,0.01587519,0.00003723261,0.00006990856,0.0004943241,0.00007944267,0.867716,0.09273875,0.002510689,0.02006606,0.00007213154],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09305167,0.0004541434,0.8718734,0.0002033021,0.0001859129,0.000319179,0.0008721872,0.0234554,0.009584783],"genre_scores_gemma":[0.5301194,0.0002438714,0.4577189,0.0002163659,0.00006227969,0.0002711459,0.001424537,0.0002079184,0.009735531],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003797543,"threshold_uncertainty_score":0.01270401,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009006641324054895,"score_gpt":0.2440005195828142,"score_spread":0.2349938782587593,"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."}}