{"id":"W6892384953","doi":"10.5061/dryad.dv41ns1xn","title":"Impatiens glandulifera SNP and SilicoDArT genotyping data","year":2021,"lang":"en","type":"dataset","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Gene flow; SNP; Genotyping; UPGMA; Genetic variation; SNP genotyping; Phylogenetic tree; SNP array; Linkage disequilibrium","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0004212021,0.0002529593,0.0003275846,0.001526786,0.0003225835,0.0004104696,0.0002903031,0.0002760871,0.001345895],"category_scores_gemma":[0.0007040892,0.0001141585,0.0006264324,0.001654251,0.0001917541,0.0001109684,0.0003477918,0.0003175875,0.0006740883],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002392531,"about_ca_system_score_gemma":0.0002380816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004568622,"about_ca_topic_score_gemma":0.007202389,"domain_scores_codex":[0.9996524,0.00004336632,0.0000288436,0.0001311134,0.0001058731,0.00003852128],"domain_scores_gemma":[0.9995742,0.0000965697,0.0001093771,0.00007389025,0.00009535528,0.00005057813],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0009222075,0.0002170047,0.1930533,0.0003315832,0.0001811644,0.001240018,0.002273634,0.002860829,0.7489077,0.0007428025,0.0004174839,0.04885217],"study_design_scores_gemma":[0.00002958005,0.000158111,0.9525069,0.00002978364,0.0001734252,0.001416754,0.0003880328,0.003009402,0.02900496,0.0002501003,0.01299226,0.00004080934],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"dataset","genre_scores_codex":[0.9842747,0.0001372001,0.004348423,0.00003626552,0.000006913302,0.00005113722,0.008867739,0.00006999429,0.00220762],"genre_scores_gemma":[0.9506498,0.000183481,0.01793269,0.00007756534,0.00001397818,0.00008973983,0.02947642,0.00003270375,0.001543609],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.004568622,"threshold_uncertainty_score":0.009084105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1515399504490009,"score_gpt":0.3852716923504997,"score_spread":0.2337317419014988,"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."}}