{"id":"W4393470779","doi":"10.5281/zenodo.6354815","title":"Seedlot Selection Tool Climate Data","year":2022,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Rangeland and Wildlife Management","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Selection (genetic algorithm); Computer science; Artificial intelligence","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":[0.000938741,0.0009614624,0.000800137,0.002221695,0.0005811775,0.00109457,0.001309586,0.0007248475,0.08601288],"category_scores_gemma":[0.004342054,0.0004794611,0.0005810105,0.004855318,0.0001875827,0.0008184661,0.0009593561,0.0009839138,0.06750859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008259616,"about_ca_system_score_gemma":0.00125215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01420536,"about_ca_topic_score_gemma":0.02950246,"domain_scores_codex":[0.9992427,0.0001285951,0.0000885974,0.000235796,0.0002070015,0.00009731885],"domain_scores_gemma":[0.9975716,0.0006462614,0.0002726477,0.0005371263,0.0007857953,0.000186464],"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":[0.00005821344,0.00002189622,0.002645302,0.0002148714,0.00001754483,0.00002405297,0.00003122485,0.0004122976,0.0001534823,0.0004350107,0.9927897,0.003196379],"study_design_scores_gemma":[0.0001497914,0.00001706436,0.0136138,0.0001438247,0.00001732411,0.00004493257,0.0001050912,0.0008210168,0.0005239591,0.00101558,0.9835184,0.00002931602],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0003086635,0.00001197825,0.0001395727,0.00002284123,0.00000997282,0.00001274264,0.9982238,0.0003032458,0.0009671066],"genre_scores_gemma":[0.0008968967,0.00001688853,0.0006273251,0.00003341029,0.000004772956,0.0001139395,0.9972627,0.0001670534,0.0008769063],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.08601288,"threshold_uncertainty_score":0.2877418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03752716661937712,"score_gpt":0.2471540657905998,"score_spread":0.2096268991712227,"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."}}