{"id":"W4288337157","doi":"","title":"Pedigree Inferences and Climate Adaptation in Scandinavian Heirloom Apple Cultivars","year":2019,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Horticultural and Viticultural Research","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"","keywords":"Adaptation (eye); Cultivar; Computer science; Biology; Horticulture","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":[0.003110089,0.0003407154,0.0003932251,0.003236457,0.000598648,0.001406751,0.0006045356,0.0004687465,0.006995665],"category_scores_gemma":[0.01367328,0.0003355143,0.0004761542,0.002553979,0.0004024628,0.0007542425,0.000687723,0.0003065779,0.0007604916],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008162005,"about_ca_system_score_gemma":0.0009217299,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01578361,"about_ca_topic_score_gemma":0.035222,"domain_scores_codex":[0.9982609,0.0008023267,0.000141045,0.0004960828,0.0001927765,0.0001068363],"domain_scores_gemma":[0.9877841,0.009851991,0.0006289065,0.0008670451,0.000617402,0.0002505961],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0018066,0.0002247609,0.6853737,0.0005725787,0.00113164,0.004729934,0.009928106,0.04324842,0.0217455,0.01161659,0.005343114,0.214279],"study_design_scores_gemma":[0.0001287038,0.0001024495,0.9535964,0.0002445834,0.0004120378,0.0007831716,0.001261525,0.02297385,0.001918206,0.006914574,0.01159689,0.00006764763],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9842094,0.0005834676,0.007954123,0.0001399306,0.00002203235,0.00002367537,0.002643145,0.00008426002,0.004339784],"genre_scores_gemma":[0.9852899,0.0004149095,0.00603035,0.00004628135,0.00001738771,0.00002102822,0.006078452,0.0001071676,0.001994666],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01578361,"threshold_uncertainty_score":0.03138345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03427781730012218,"score_gpt":0.2556779213008883,"score_spread":0.2214001040007661,"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."}}