{"id":"W4413260195","doi":"10.1270/jsbbs.25011","title":"Inheritance characteristics and potential of genomic prediction for pungency levels in F<sub>1</sub> progeny of chili pepper (<i>Capsicum annuum</i>)","year":2025,"lang":"en","type":"article","venue":"Breeding Science","topic":"Agricultural Practices and Plant Genetics","field":"Agricultural and Biological Sciences","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Japan Society for the Promotion of Science; Institute of Genetics; University of Tokyo","keywords":"Pungency; Capsicum annuum; Biology; Pepper; Chili pepper; Horticulture; Inheritance (genetic algorithm); Genetics; Botany; Biotechnology; Gene","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0004894075,0.000343363,0.0001994882,0.0004140364,0.0001100921,0.0002622881,0.0002025975,0.00021541,0.0004840061],"category_scores_gemma":[0.0006885395,0.0001661679,0.0003086498,0.0002471059,0.0001070557,0.0001218876,0.0001669685,0.0004018501,0.0001916152],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001939195,"about_ca_system_score_gemma":0.0001428566,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001923167,"about_ca_topic_score_gemma":0.002196254,"domain_scores_codex":[0.9998351,0.00003575777,0.000009697208,0.00007719225,0.00002708863,0.00001515887],"domain_scores_gemma":[0.9993677,0.000368352,0.0001234573,0.0000515494,0.00004361023,0.00004543738],"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":[0.0002807862,0.0001180299,0.1071795,0.00004158354,0.00008335323,0.0002378052,0.0001467246,0.004877391,0.8710788,0.0001424043,0.00006018453,0.01575326],"study_design_scores_gemma":[0.00002495793,0.0005327378,0.7943137,0.00001383469,0.0001917183,0.0006914974,0.000122747,0.04260451,0.1602209,0.0002239478,0.001001931,0.00005761513],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948224,0.00005787984,0.004472066,0.00001243505,0.00000158773,0.000005425656,0.0003945246,0.00005679018,0.00017697],"genre_scores_gemma":[0.9937791,0.00005363909,0.004598017,0.00001502778,0.000001798333,0.000009788901,0.001248398,0.00003482519,0.0002594229],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001923167,"threshold_uncertainty_score":0.003823876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01861256160489498,"score_gpt":0.2235979794152061,"score_spread":0.2049854178103111,"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."}}