{"id":"W4395237052","doi":"10.15468/4t4bm5","title":"Nordic crop wild relative priority list","year":2023,"lang":"en","type":"dataset","venue":"Open MIND","topic":"Sustainable Agricultural Systems Analysis","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nordic Life Science Pipeline (Canada)","funders":"","keywords":"Crop; Geography; Environmental science; Forestry","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0005770827,0.0003296585,0.0005021946,0.00005247557,0.000297487,0.0003914527,0.00159075,0.0003049093,0.02134373],"category_scores_gemma":[0.0001949395,0.0002406169,0.0001507299,0.001162624,0.0001486511,0.0005520871,0.002368651,0.0004373967,0.09684706],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007984792,"about_ca_system_score_gemma":0.00004144555,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.1063257,"about_ca_topic_score_gemma":0.03588345,"domain_scores_codex":[0.9976059,0.0001649867,0.0004053711,0.0008413923,0.0005620956,0.0004202908],"domain_scores_gemma":[0.9986088,0.00009745186,0.0003578986,0.0007476915,0.00002097579,0.0001671994],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000005192211,0.0000333498,0.0004627835,0.000009467934,0.00007748491,0.0001451627,0.00006500234,0.00006207552,0.00001073384,2.005852e-7,0.996829,0.002299511],"study_design_scores_gemma":[0.0001320127,0.00003291238,0.00422175,0.00004868254,0.0001807642,0.000007179511,0.000283613,0.000009378348,0.00000959435,0.00001792991,0.9946806,0.0003755996],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.006703987,0.00003580352,0.00000134034,0.0002776962,0.0002563859,0.0009784079,0.9845756,0.000004382414,0.007166426],"genre_scores_gemma":[0.0002066828,0.00004374239,0.0001804569,0.00003582934,0.000149101,0.00006329675,0.9211605,0.00001814916,0.0781422],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.07550333,"threshold_uncertainty_score":0.9817092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0204493214187531,"score_gpt":0.2824473915991801,"score_spread":0.261998070180427,"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."}}