{"id":"W2037130097","doi":"10.1016/j.biosystemseng.2006.10.018","title":"Stem-end/Calyx Identification on Apples using Contour Analysis in Multispectral Images","year":2007,"lang":"en","type":"article","venue":"Biosystems Engineering","topic":"Postharvest Quality and Shelf Life Management","field":"Agricultural and Biological Sciences","cited_by":49,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"","keywords":"Calyx; Multispectral image; Principal component analysis; Hyperspectral imaging; Cultivar; Artificial intelligence; Horticulture; 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":[0.001048295,0.0001367903,0.0002255114,0.0001216605,0.00006112075,0.00009605912,0.0001591425,0.00006998066,0.00001656428],"category_scores_gemma":[0.00001897098,0.00006846851,0.0001096781,0.0008699415,0.000009964576,0.0001149891,0.00002681807,0.00008408882,0.00001365243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009940868,"about_ca_system_score_gemma":0.00000172006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009564335,"about_ca_topic_score_gemma":0.001712231,"domain_scores_codex":[0.9987549,0.0000437893,0.0004120948,0.0002669178,0.0002237433,0.0002985141],"domain_scores_gemma":[0.9996028,0.0001385217,0.00009056536,0.00007592786,0.00002060844,0.00007160465],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0000144713,0.00008831267,0.02792443,0.00006055943,0.0001313974,0.00002469683,0.0001163636,0.008328621,0.957001,0.001913616,0.00001842268,0.004378089],"study_design_scores_gemma":[0.0001598412,0.00003656921,0.9376025,0.00007831385,0.00006350531,0.000002479312,0.0006853739,0.01180871,0.04749315,0.000003818671,0.001740723,0.0003249824],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975269,0.0001176436,0.001668588,0.00008871221,0.0001724199,0.0002092535,0.00002749505,0.00009069742,0.00009822351],"genre_scores_gemma":[0.999516,0.000006049436,0.0001061911,0.00002034331,0.0001786898,0.000006211716,0.00002912383,0.000001369349,0.0001360117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9096781,"threshold_uncertainty_score":0.2792065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02990400006369654,"score_gpt":0.2447402884730056,"score_spread":0.2148362884093091,"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."}}